{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbece81f4b1399e6",
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import argparse\n",
    "from model import *\n",
    "from metric import *\n",
    "from sklearn import metrics\n",
    "import torch.nn.functional as F\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57be0adc-e87b-4d7e-bd0e-01d68e204fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "args Namespace(seed=0, epochs=500, weight_decay=0.0005, dropout=0.1, tot_updates=1000, warmup_updates=400, peak_lr=0.001, end_lr=0.0001, pe_dim=15, hops=7, graphformer_layers=1, n_heads=8, node_input=64, node_hidden=128, node_output=64, ffn_dim=256, GCNII_layers=20)\n",
      "元素为1的个数： 4763\n",
      "Dis_adj 中值为 1 的元素个数: 19007\n",
      "Meta_adj 中值为 1 的元素个数: 837881\n",
      "seed=0, evaluating metabolite-disease....\n",
      "------this is 1th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.7078 Acc: 0.5026 Pre: 0.5013 Recall: 1.0000 F1: 0.6678 Train AUC: 0.4935 Val AUC: 0.4866 Val PRC: 0.4866 Time: 1.45\n",
      "Epoch: 2 Train Loss: 0.7088 Acc: 0.5005 Pre: 0.5003 Recall: 0.9979 F1: 0.6664 Train AUC: 0.4754 Val AUC: 0.4825 Val PRC: 0.4910 Time: 0.74\n",
      "Epoch: 3 Train Loss: 0.7035 Acc: 0.5005 Pre: 0.5003 Recall: 1.0000 F1: 0.6669 Train AUC: 0.5296 Val AUC: 0.5392 Val PRC: 0.5329 Time: 0.75\n",
      "Epoch: 4 Train Loss: 0.6955 Acc: 0.5095 Pre: 0.5048 Recall: 0.9937 F1: 0.6695 Train AUC: 0.5226 Val AUC: 0.5073 Val PRC: 0.5189 Time: 0.69\n",
      "Epoch: 5 Train Loss: 0.7036 Acc: 0.5016 Pre: 0.5008 Recall: 1.0000 F1: 0.6674 Train AUC: 0.5184 Val AUC: 0.5046 Val PRC: 0.5099 Time: 0.71\n",
      "Epoch: 6 Train Loss: 0.6947 Acc: 0.5058 Pre: 0.5029 Recall: 0.9958 F1: 0.6683 Train AUC: 0.5441 Val AUC: 0.5524 Val PRC: 0.5356 Time: 0.72\n",
      "Epoch: 7 Train Loss: 0.6979 Acc: 0.5005 Pre: 0.5003 Recall: 0.9989 F1: 0.6667 Train AUC: 0.5548 Val AUC: 0.5518 Val PRC: 0.5420 Time: 0.70\n",
      "Epoch: 8 Train Loss: 0.6779 Acc: 0.5284 Pre: 0.5148 Recall: 0.9884 F1: 0.6770 Train AUC: 0.6077 Val AUC: 0.5938 Val PRC: 0.5764 Time: 0.70\n",
      "Epoch: 9 Train Loss: 0.6809 Acc: 0.5473 Pre: 0.5255 Recall: 0.9748 F1: 0.6829 Train AUC: 0.5648 Val AUC: 0.5863 Val PRC: 0.5626 Time: 0.71\n",
      "Epoch: 10 Train Loss: 0.6737 Acc: 0.5404 Pre: 0.5216 Recall: 0.9779 F1: 0.6803 Train AUC: 0.6237 Val AUC: 0.6260 Val PRC: 0.6059 Time: 0.71\n",
      "Epoch: 11 Train Loss: 0.6481 Acc: 0.6450 Pre: 0.5976 Recall: 0.8876 F1: 0.7143 Train AUC: 0.7353 Val AUC: 0.7416 Val PRC: 0.7551 Time: 0.70\n",
      "Epoch: 12 Train Loss: 0.6660 Acc: 0.5651 Pre: 0.5362 Recall: 0.9653 F1: 0.6894 Train AUC: 0.6613 Val AUC: 0.6649 Val PRC: 0.6551 Time: 0.71\n",
      "Epoch: 13 Train Loss: 0.6487 Acc: 0.6591 Pre: 0.6133 Recall: 0.8613 F1: 0.7165 Train AUC: 0.7382 Val AUC: 0.7442 Val PRC: 0.7337 Time: 0.71\n",
      "Epoch: 14 Train Loss: 0.6482 Acc: 0.6670 Pre: 0.6210 Recall: 0.8571 F1: 0.7202 Train AUC: 0.7477 Val AUC: 0.7572 Val PRC: 0.7366 Time: 0.70\n",
      "Epoch: 15 Train Loss: 0.6388 Acc: 0.6886 Pre: 0.6414 Recall: 0.8550 F1: 0.7330 Train AUC: 0.7857 Val AUC: 0.7786 Val PRC: 0.7690 Time: 0.70\n",
      "Epoch: 16 Train Loss: 0.6331 Acc: 0.6786 Pre: 0.6243 Recall: 0.8971 F1: 0.7362 Train AUC: 0.7825 Val AUC: 0.8029 Val PRC: 0.8073 Time: 0.72\n",
      "Epoch: 17 Train Loss: 0.6293 Acc: 0.6901 Pre: 0.6453 Recall: 0.8445 F1: 0.7316 Train AUC: 0.7985 Val AUC: 0.7993 Val PRC: 0.8025 Time: 0.72\n",
      "Epoch: 18 Train Loss: 0.6225 Acc: 0.6712 Pre: 0.6171 Recall: 0.9023 F1: 0.7329 Train AUC: 0.7910 Val AUC: 0.7994 Val PRC: 0.8087 Time: 0.71\n",
      "Epoch: 19 Train Loss: 0.6083 Acc: 0.6922 Pre: 0.6395 Recall: 0.8813 F1: 0.7412 Train AUC: 0.8020 Val AUC: 0.8046 Val PRC: 0.8191 Time: 0.72\n",
      "Epoch: 20 Train Loss: 0.6005 Acc: 0.7190 Pre: 0.6827 Recall: 0.8183 F1: 0.7444 Train AUC: 0.8079 Val AUC: 0.8214 Val PRC: 0.8370 Time: 0.71\n",
      "Epoch: 21 Train Loss: 0.6067 Acc: 0.6985 Pre: 0.6463 Recall: 0.8771 F1: 0.7442 Train AUC: 0.7958 Val AUC: 0.8040 Val PRC: 0.8150 Time: 0.71\n",
      "Epoch: 22 Train Loss: 0.5921 Acc: 0.7285 Pre: 0.6910 Recall: 0.8267 F1: 0.7527 Train AUC: 0.8067 Val AUC: 0.8237 Val PRC: 0.8363 Time: 0.75\n",
      "Epoch: 23 Train Loss: 0.5846 Acc: 0.7553 Pre: 0.7531 Recall: 0.7595 F1: 0.7563 Train AUC: 0.8213 Val AUC: 0.8380 Val PRC: 0.8539 Time: 0.73\n",
      "Epoch: 24 Train Loss: 0.5763 Acc: 0.7574 Pre: 0.7629 Recall: 0.7468 F1: 0.7548 Train AUC: 0.8207 Val AUC: 0.8401 Val PRC: 0.8596 Time: 0.75\n",
      "Epoch: 25 Train Loss: 0.5644 Acc: 0.7269 Pre: 0.6753 Recall: 0.8739 F1: 0.7619 Train AUC: 0.8271 Val AUC: 0.8360 Val PRC: 0.8479 Time: 0.75\n",
      "Epoch: 26 Train Loss: 0.5586 Acc: 0.7668 Pre: 0.7867 Recall: 0.7321 F1: 0.7584 Train AUC: 0.8254 Val AUC: 0.8382 Val PRC: 0.8605 Time: 0.76\n",
      "Epoch: 27 Train Loss: 0.5511 Acc: 0.7395 Pre: 0.7115 Recall: 0.8057 F1: 0.7557 Train AUC: 0.8352 Val AUC: 0.8433 Val PRC: 0.8655 Time: 0.72\n",
      "Epoch: 28 Train Loss: 0.5414 Acc: 0.7652 Pre: 0.7892 Recall: 0.7237 F1: 0.7551 Train AUC: 0.8349 Val AUC: 0.8406 Val PRC: 0.8597 Time: 0.75\n",
      "Epoch: 29 Train Loss: 0.5427 Acc: 0.7568 Pre: 0.7621 Recall: 0.7468 F1: 0.7544 Train AUC: 0.8279 Val AUC: 0.8411 Val PRC: 0.8593 Time: 0.86\n",
      "Epoch: 30 Train Loss: 0.5323 Acc: 0.7642 Pre: 0.7684 Recall: 0.7563 F1: 0.7623 Train AUC: 0.8358 Val AUC: 0.8480 Val PRC: 0.8693 Time: 0.86\n",
      "Epoch: 31 Train Loss: 0.5197 Acc: 0.7626 Pre: 0.7485 Recall: 0.7910 F1: 0.7692 Train AUC: 0.8440 Val AUC: 0.8517 Val PRC: 0.8727 Time: 0.82\n",
      "Epoch: 32 Train Loss: 0.5180 Acc: 0.7647 Pre: 0.7636 Recall: 0.7668 F1: 0.7652 Train AUC: 0.8352 Val AUC: 0.8501 Val PRC: 0.8729 Time: 0.83\n",
      "Epoch: 33 Train Loss: 0.5225 Acc: 0.7584 Pre: 0.7573 Recall: 0.7605 F1: 0.7589 Train AUC: 0.8346 Val AUC: 0.8476 Val PRC: 0.8678 Time: 0.90\n",
      "Epoch: 34 Train Loss: 0.5102 Acc: 0.7637 Pre: 0.7827 Recall: 0.7300 F1: 0.7554 Train AUC: 0.8409 Val AUC: 0.8476 Val PRC: 0.8691 Time: 0.74\n",
      "Epoch: 35 Train Loss: 0.5104 Acc: 0.7637 Pre: 0.7728 Recall: 0.7468 F1: 0.7596 Train AUC: 0.8373 Val AUC: 0.8472 Val PRC: 0.8680 Time: 0.72\n",
      "Epoch: 36 Train Loss: 0.5024 Acc: 0.7605 Pre: 0.7557 Recall: 0.7700 F1: 0.7627 Train AUC: 0.8431 Val AUC: 0.8553 Val PRC: 0.8775 Time: 0.74\n",
      "Epoch: 37 Train Loss: 0.4970 Acc: 0.7742 Pre: 0.7849 Recall: 0.7553 F1: 0.7698 Train AUC: 0.8487 Val AUC: 0.8612 Val PRC: 0.8815 Time: 0.73\n",
      "Epoch: 38 Train Loss: 0.4895 Acc: 0.7710 Pre: 0.7590 Recall: 0.7941 F1: 0.7762 Train AUC: 0.8533 Val AUC: 0.8672 Val PRC: 0.8852 Time: 0.72\n",
      "Epoch: 39 Train Loss: 0.4802 Acc: 0.7658 Pre: 0.7500 Recall: 0.7973 F1: 0.7729 Train AUC: 0.8615 Val AUC: 0.8672 Val PRC: 0.8865 Time: 0.73\n",
      "Epoch: 40 Train Loss: 0.4801 Acc: 0.7799 Pre: 0.7838 Recall: 0.7731 F1: 0.7784 Train AUC: 0.8568 Val AUC: 0.8691 Val PRC: 0.8856 Time: 0.72\n",
      "Epoch: 41 Train Loss: 0.4830 Acc: 0.7447 Pre: 0.7069 Recall: 0.8361 F1: 0.7661 Train AUC: 0.8522 Val AUC: 0.8635 Val PRC: 0.8841 Time: 0.73\n",
      "Epoch: 42 Train Loss: 0.4695 Acc: 0.7915 Pre: 0.8087 Recall: 0.7637 F1: 0.7855 Train AUC: 0.8644 Val AUC: 0.8766 Val PRC: 0.8963 Time: 0.72\n",
      "Epoch: 43 Train Loss: 0.4630 Acc: 0.7710 Pre: 0.7500 Recall: 0.8130 F1: 0.7802 Train AUC: 0.8663 Val AUC: 0.8739 Val PRC: 0.8942 Time: 0.71\n",
      "Epoch: 44 Train Loss: 0.4547 Acc: 0.7931 Pre: 0.8039 Recall: 0.7752 F1: 0.7893 Train AUC: 0.8729 Val AUC: 0.8814 Val PRC: 0.9014 Time: 0.71\n",
      "Epoch: 45 Train Loss: 0.4547 Acc: 0.7852 Pre: 0.7987 Recall: 0.7626 F1: 0.7802 Train AUC: 0.8671 Val AUC: 0.8769 Val PRC: 0.8975 Time: 0.72\n",
      "Epoch: 46 Train Loss: 0.4532 Acc: 0.8072 Pre: 0.8698 Recall: 0.7227 F1: 0.7894 Train AUC: 0.8655 Val AUC: 0.8811 Val PRC: 0.9017 Time: 0.73\n",
      "Epoch: 47 Train Loss: 0.4425 Acc: 0.8046 Pre: 0.8118 Recall: 0.7931 F1: 0.8023 Train AUC: 0.8767 Val AUC: 0.8859 Val PRC: 0.9057 Time: 0.71\n",
      "Epoch: 48 Train Loss: 0.4383 Acc: 0.8162 Pre: 0.8909 Recall: 0.7206 F1: 0.7967 Train AUC: 0.8754 Val AUC: 0.8831 Val PRC: 0.9042 Time: 0.71\n",
      "Epoch: 49 Train Loss: 0.4321 Acc: 0.8235 Pre: 0.9021 Recall: 0.7258 F1: 0.8044 Train AUC: 0.8806 Val AUC: 0.8890 Val PRC: 0.9106 Time: 0.71\n",
      "Epoch: 50 Train Loss: 0.4270 Acc: 0.8272 Pre: 0.9137 Recall: 0.7227 F1: 0.8070 Train AUC: 0.8828 Val AUC: 0.8882 Val PRC: 0.9088 Time: 0.72\n",
      "Epoch: 51 Train Loss: 0.4242 Acc: 0.8314 Pre: 0.9269 Recall: 0.7195 F1: 0.8102 Train AUC: 0.8838 Val AUC: 0.8920 Val PRC: 0.9122 Time: 0.74\n",
      "Epoch: 52 Train Loss: 0.4248 Acc: 0.8283 Pre: 0.9346 Recall: 0.7059 F1: 0.8043 Train AUC: 0.8807 Val AUC: 0.8880 Val PRC: 0.9086 Time: 0.74\n",
      "Epoch: 53 Train Loss: 0.4122 Acc: 0.8309 Pre: 0.8889 Recall: 0.7563 F1: 0.8173 Train AUC: 0.8910 Val AUC: 0.8933 Val PRC: 0.9132 Time: 0.75\n",
      "Epoch: 54 Train Loss: 0.4138 Acc: 0.8314 Pre: 0.8999 Recall: 0.7458 F1: 0.8156 Train AUC: 0.8861 Val AUC: 0.8928 Val PRC: 0.9133 Time: 0.73\n",
      "Epoch: 55 Train Loss: 0.4143 Acc: 0.8372 Pre: 0.9397 Recall: 0.7206 F1: 0.8157 Train AUC: 0.8874 Val AUC: 0.8986 Val PRC: 0.9180 Time: 0.74\n",
      "Epoch: 56 Train Loss: 0.4080 Acc: 0.8293 Pre: 0.8710 Recall: 0.7731 F1: 0.8191 Train AUC: 0.8913 Val AUC: 0.8982 Val PRC: 0.9175 Time: 0.76\n",
      "Epoch: 57 Train Loss: 0.3992 Acc: 0.8330 Pre: 0.8962 Recall: 0.7532 F1: 0.8185 Train AUC: 0.8941 Val AUC: 0.8981 Val PRC: 0.9175 Time: 0.72\n",
      "Epoch: 58 Train Loss: 0.3972 Acc: 0.8325 Pre: 0.8874 Recall: 0.7616 F1: 0.8197 Train AUC: 0.8965 Val AUC: 0.9021 Val PRC: 0.9203 Time: 0.72\n",
      "Epoch: 59 Train Loss: 0.3974 Acc: 0.8367 Pre: 0.9314 Recall: 0.7269 F1: 0.8165 Train AUC: 0.8981 Val AUC: 0.9023 Val PRC: 0.9191 Time: 0.73\n",
      "Epoch: 60 Train Loss: 0.3829 Acc: 0.8214 Pre: 0.8333 Recall: 0.8036 F1: 0.8182 Train AUC: 0.9088 Val AUC: 0.9041 Val PRC: 0.9200 Time: 0.71\n",
      "Epoch: 61 Train Loss: 0.3768 Acc: 0.8325 Pre: 0.8659 Recall: 0.7868 F1: 0.8244 Train AUC: 0.9086 Val AUC: 0.9052 Val PRC: 0.9213 Time: 0.71\n",
      "Epoch: 62 Train Loss: 0.3803 Acc: 0.8361 Pre: 0.8837 Recall: 0.7742 F1: 0.8253 Train AUC: 0.9105 Val AUC: 0.9090 Val PRC: 0.9245 Time: 0.71\n",
      "Epoch: 63 Train Loss: 0.3766 Acc: 0.8435 Pre: 0.8625 Recall: 0.8172 F1: 0.8393 Train AUC: 0.9103 Val AUC: 0.9145 Val PRC: 0.9300 Time: 0.73\n",
      "Epoch: 64 Train Loss: 0.3678 Acc: 0.8498 Pre: 0.8793 Recall: 0.8109 F1: 0.8437 Train AUC: 0.9169 Val AUC: 0.9192 Val PRC: 0.9320 Time: 0.71\n",
      "Epoch: 65 Train Loss: 0.3658 Acc: 0.8508 Pre: 0.9005 Recall: 0.7889 F1: 0.8410 Train AUC: 0.9172 Val AUC: 0.9177 Val PRC: 0.9318 Time: 0.72\n",
      "Epoch: 66 Train Loss: 0.3641 Acc: 0.8435 Pre: 0.8699 Recall: 0.8078 F1: 0.8377 Train AUC: 0.9158 Val AUC: 0.9157 Val PRC: 0.9310 Time: 0.73\n",
      "Epoch: 67 Train Loss: 0.3580 Acc: 0.8388 Pre: 0.8420 Recall: 0.8340 F1: 0.8380 Train AUC: 0.9183 Val AUC: 0.9171 Val PRC: 0.9317 Time: 0.71\n",
      "Epoch: 68 Train Loss: 0.3498 Acc: 0.8529 Pre: 0.8758 Recall: 0.8225 F1: 0.8483 Train AUC: 0.9239 Val AUC: 0.9232 Val PRC: 0.9357 Time: 0.72\n",
      "Epoch: 69 Train Loss: 0.3614 Acc: 0.8382 Pre: 0.8448 Recall: 0.8288 F1: 0.8367 Train AUC: 0.9171 Val AUC: 0.9215 Val PRC: 0.9349 Time: 0.73\n",
      "Epoch: 70 Train Loss: 0.3566 Acc: 0.8529 Pre: 0.8801 Recall: 0.8172 F1: 0.8475 Train AUC: 0.9214 Val AUC: 0.9234 Val PRC: 0.9353 Time: 0.73\n",
      "Epoch: 71 Train Loss: 0.3420 Acc: 0.8498 Pre: 0.8684 Recall: 0.8246 F1: 0.8459 Train AUC: 0.9259 Val AUC: 0.9207 Val PRC: 0.9353 Time: 0.72\n",
      "Epoch: 72 Train Loss: 0.3418 Acc: 0.8529 Pre: 0.8750 Recall: 0.8235 F1: 0.8485 Train AUC: 0.9262 Val AUC: 0.9250 Val PRC: 0.9372 Time: 0.72\n",
      "Epoch: 73 Train Loss: 0.3558 Acc: 0.8556 Pre: 0.8791 Recall: 0.8246 F1: 0.8509 Train AUC: 0.9206 Val AUC: 0.9256 Val PRC: 0.9389 Time: 0.72\n",
      "Epoch: 74 Train Loss: 0.3329 Acc: 0.8503 Pre: 0.8559 Recall: 0.8424 F1: 0.8491 Train AUC: 0.9319 Val AUC: 0.9245 Val PRC: 0.9377 Time: 0.73\n",
      "Epoch: 75 Train Loss: 0.3379 Acc: 0.8645 Pre: 0.8961 Recall: 0.8246 F1: 0.8589 Train AUC: 0.9277 Val AUC: 0.9299 Val PRC: 0.9429 Time: 0.72\n",
      "Epoch: 76 Train Loss: 0.3321 Acc: 0.8619 Pre: 0.9001 Recall: 0.8141 F1: 0.8549 Train AUC: 0.9315 Val AUC: 0.9335 Val PRC: 0.9441 Time: 0.73\n",
      "Epoch: 77 Train Loss: 0.3210 Acc: 0.8592 Pre: 0.8878 Recall: 0.8225 F1: 0.8539 Train AUC: 0.9345 Val AUC: 0.9326 Val PRC: 0.9433 Time: 0.73\n",
      "Epoch: 78 Train Loss: 0.3297 Acc: 0.8556 Pre: 0.8732 Recall: 0.8319 F1: 0.8521 Train AUC: 0.9313 Val AUC: 0.9291 Val PRC: 0.9414 Time: 0.71\n",
      "Epoch: 79 Train Loss: 0.3363 Acc: 0.8608 Pre: 0.8698 Recall: 0.8487 F1: 0.8591 Train AUC: 0.9291 Val AUC: 0.9344 Val PRC: 0.9444 Time: 0.71\n",
      "Epoch: 80 Train Loss: 0.3279 Acc: 0.8556 Pre: 0.8416 Recall: 0.8761 F1: 0.8585 Train AUC: 0.9338 Val AUC: 0.9345 Val PRC: 0.9445 Time: 0.71\n",
      "Epoch: 81 Train Loss: 0.3210 Acc: 0.8724 Pre: 0.9156 Recall: 0.8204 F1: 0.8654 Train AUC: 0.9370 Val AUC: 0.9341 Val PRC: 0.9455 Time: 0.74\n",
      "Epoch: 82 Train Loss: 0.3195 Acc: 0.8682 Pre: 0.8717 Recall: 0.8634 F1: 0.8675 Train AUC: 0.9370 Val AUC: 0.9376 Val PRC: 0.9465 Time: 0.74\n",
      "Epoch: 83 Train Loss: 0.3249 Acc: 0.8535 Pre: 0.8538 Recall: 0.8529 F1: 0.8534 Train AUC: 0.9355 Val AUC: 0.9363 Val PRC: 0.9451 Time: 0.70\n",
      "Epoch: 84 Train Loss: 0.3224 Acc: 0.8671 Pre: 0.8683 Recall: 0.8655 F1: 0.8669 Train AUC: 0.9361 Val AUC: 0.9378 Val PRC: 0.9465 Time: 0.73\n",
      "Epoch: 85 Train Loss: 0.3194 Acc: 0.8608 Pre: 0.8398 Recall: 0.8918 F1: 0.8650 Train AUC: 0.9375 Val AUC: 0.9403 Val PRC: 0.9478 Time: 0.73\n",
      "Epoch: 86 Train Loss: 0.3077 Acc: 0.8745 Pre: 0.8854 Recall: 0.8603 F1: 0.8727 Train AUC: 0.9423 Val AUC: 0.9410 Val PRC: 0.9494 Time: 0.71\n",
      "Epoch: 87 Train Loss: 0.3065 Acc: 0.8792 Pre: 0.9047 Recall: 0.8477 F1: 0.8753 Train AUC: 0.9425 Val AUC: 0.9405 Val PRC: 0.9488 Time: 0.71\n",
      "Epoch: 88 Train Loss: 0.3108 Acc: 0.8692 Pre: 0.8635 Recall: 0.8771 F1: 0.8702 Train AUC: 0.9407 Val AUC: 0.9425 Val PRC: 0.9484 Time: 0.73\n",
      "Epoch: 89 Train Loss: 0.3129 Acc: 0.8755 Pre: 0.8950 Recall: 0.8508 F1: 0.8724 Train AUC: 0.9420 Val AUC: 0.9438 Val PRC: 0.9513 Time: 0.71\n",
      "Epoch: 90 Train Loss: 0.3002 Acc: 0.8755 Pre: 0.8743 Recall: 0.8771 F1: 0.8757 Train AUC: 0.9452 Val AUC: 0.9461 Val PRC: 0.9520 Time: 0.70\n",
      "Epoch: 91 Train Loss: 0.3044 Acc: 0.8787 Pre: 0.8906 Recall: 0.8634 F1: 0.8768 Train AUC: 0.9441 Val AUC: 0.9454 Val PRC: 0.9526 Time: 0.72\n",
      "Epoch: 92 Train Loss: 0.3002 Acc: 0.8787 Pre: 0.8983 Recall: 0.8540 F1: 0.8756 Train AUC: 0.9452 Val AUC: 0.9462 Val PRC: 0.9529 Time: 0.71\n",
      "Epoch: 93 Train Loss: 0.2975 Acc: 0.8845 Pre: 0.8935 Recall: 0.8729 F1: 0.8831 Train AUC: 0.9468 Val AUC: 0.9507 Val PRC: 0.9562 Time: 0.70\n",
      "Epoch: 94 Train Loss: 0.2970 Acc: 0.8813 Pre: 0.9015 Recall: 0.8561 F1: 0.8782 Train AUC: 0.9469 Val AUC: 0.9493 Val PRC: 0.9546 Time: 0.73\n",
      "Epoch: 95 Train Loss: 0.2886 Acc: 0.8818 Pre: 0.8981 Recall: 0.8613 F1: 0.8794 Train AUC: 0.9503 Val AUC: 0.9484 Val PRC: 0.9537 Time: 0.71\n",
      "Epoch: 96 Train Loss: 0.2920 Acc: 0.8761 Pre: 0.8706 Recall: 0.8834 F1: 0.8770 Train AUC: 0.9483 Val AUC: 0.9477 Val PRC: 0.9533 Time: 0.72\n",
      "Epoch: 97 Train Loss: 0.2904 Acc: 0.8750 Pre: 0.8703 Recall: 0.8813 F1: 0.8758 Train AUC: 0.9485 Val AUC: 0.9477 Val PRC: 0.9530 Time: 0.72\n",
      "Epoch: 98 Train Loss: 0.2846 Acc: 0.8834 Pre: 0.8842 Recall: 0.8824 F1: 0.8833 Train AUC: 0.9514 Val AUC: 0.9515 Val PRC: 0.9572 Time: 0.71\n",
      "Epoch: 99 Train Loss: 0.2892 Acc: 0.8808 Pre: 0.8756 Recall: 0.8876 F1: 0.8816 Train AUC: 0.9495 Val AUC: 0.9498 Val PRC: 0.9557 Time: 0.71\n",
      "Epoch: 100 Train Loss: 0.2831 Acc: 0.8845 Pre: 0.9004 Recall: 0.8645 F1: 0.8821 Train AUC: 0.9521 Val AUC: 0.9522 Val PRC: 0.9570 Time: 0.70\n",
      "Epoch: 101 Train Loss: 0.2887 Acc: 0.8855 Pre: 0.8921 Recall: 0.8771 F1: 0.8845 Train AUC: 0.9501 Val AUC: 0.9526 Val PRC: 0.9564 Time: 0.70\n",
      "Epoch: 102 Train Loss: 0.2819 Acc: 0.8782 Pre: 0.8863 Recall: 0.8676 F1: 0.8769 Train AUC: 0.9527 Val AUC: 0.9507 Val PRC: 0.9562 Time: 0.71\n",
      "Epoch: 103 Train Loss: 0.2824 Acc: 0.8824 Pre: 0.8753 Recall: 0.8918 F1: 0.8835 Train AUC: 0.9522 Val AUC: 0.9525 Val PRC: 0.9574 Time: 0.72\n",
      "Epoch: 104 Train Loss: 0.2733 Acc: 0.8902 Pre: 0.8923 Recall: 0.8876 F1: 0.8899 Train AUC: 0.9550 Val AUC: 0.9563 Val PRC: 0.9595 Time: 0.71\n",
      "Epoch: 105 Train Loss: 0.2826 Acc: 0.8792 Pre: 0.8737 Recall: 0.8866 F1: 0.8801 Train AUC: 0.9517 Val AUC: 0.9528 Val PRC: 0.9579 Time: 0.74\n",
      "Epoch: 106 Train Loss: 0.2834 Acc: 0.8887 Pre: 0.8978 Recall: 0.8771 F1: 0.8874 Train AUC: 0.9516 Val AUC: 0.9537 Val PRC: 0.9582 Time: 0.73\n",
      "Epoch: 107 Train Loss: 0.2754 Acc: 0.8850 Pre: 0.8751 Recall: 0.8981 F1: 0.8865 Train AUC: 0.9542 Val AUC: 0.9543 Val PRC: 0.9573 Time: 0.70\n",
      "Epoch: 108 Train Loss: 0.2736 Acc: 0.8813 Pre: 0.8878 Recall: 0.8729 F1: 0.8803 Train AUC: 0.9557 Val AUC: 0.9545 Val PRC: 0.9581 Time: 0.70\n",
      "Epoch: 109 Train Loss: 0.2727 Acc: 0.8902 Pre: 0.8906 Recall: 0.8897 F1: 0.8902 Train AUC: 0.9555 Val AUC: 0.9581 Val PRC: 0.9615 Time: 0.72\n",
      "Epoch: 110 Train Loss: 0.2730 Acc: 0.8776 Pre: 0.8542 Recall: 0.9107 F1: 0.8815 Train AUC: 0.9553 Val AUC: 0.9568 Val PRC: 0.9597 Time: 0.71\n",
      "Epoch: 111 Train Loss: 0.2684 Acc: 0.8829 Pre: 0.8686 Recall: 0.9023 F1: 0.8851 Train AUC: 0.9572 Val AUC: 0.9546 Val PRC: 0.9579 Time: 0.74\n",
      "Epoch: 112 Train Loss: 0.2710 Acc: 0.8787 Pre: 0.8775 Recall: 0.8803 F1: 0.8789 Train AUC: 0.9556 Val AUC: 0.9545 Val PRC: 0.9568 Time: 0.72\n",
      "Epoch: 113 Train Loss: 0.2585 Acc: 0.8881 Pre: 0.8837 Recall: 0.8939 F1: 0.8888 Train AUC: 0.9603 Val AUC: 0.9588 Val PRC: 0.9616 Time: 0.72\n",
      "Epoch: 114 Train Loss: 0.2725 Acc: 0.8918 Pre: 0.9135 Recall: 0.8655 F1: 0.8889 Train AUC: 0.9555 Val AUC: 0.9586 Val PRC: 0.9620 Time: 0.70\n",
      "Epoch: 115 Train Loss: 0.2706 Acc: 0.8918 Pre: 0.9172 Recall: 0.8613 F1: 0.8884 Train AUC: 0.9565 Val AUC: 0.9572 Val PRC: 0.9603 Time: 0.72\n",
      "Epoch: 116 Train Loss: 0.2662 Acc: 0.8892 Pre: 0.9112 Recall: 0.8624 F1: 0.8861 Train AUC: 0.9572 Val AUC: 0.9561 Val PRC: 0.9600 Time: 0.70\n",
      "Epoch: 117 Train Loss: 0.2586 Acc: 0.8944 Pre: 0.8965 Recall: 0.8918 F1: 0.8942 Train AUC: 0.9598 Val AUC: 0.9573 Val PRC: 0.9602 Time: 0.70\n",
      "Epoch: 118 Train Loss: 0.2632 Acc: 0.8887 Pre: 0.9139 Recall: 0.8582 F1: 0.8852 Train AUC: 0.9586 Val AUC: 0.9578 Val PRC: 0.9613 Time: 0.71\n",
      "Epoch: 119 Train Loss: 0.2610 Acc: 0.8913 Pre: 0.8992 Recall: 0.8813 F1: 0.8902 Train AUC: 0.9595 Val AUC: 0.9573 Val PRC: 0.9600 Time: 0.74\n",
      "Epoch: 120 Train Loss: 0.2591 Acc: 0.8908 Pre: 0.9124 Recall: 0.8645 F1: 0.8878 Train AUC: 0.9596 Val AUC: 0.9593 Val PRC: 0.9617 Time: 0.71\n",
      "Epoch: 121 Train Loss: 0.2572 Acc: 0.8855 Pre: 0.8700 Recall: 0.9065 F1: 0.8879 Train AUC: 0.9608 Val AUC: 0.9621 Val PRC: 0.9643 Time: 0.70\n",
      "Epoch: 122 Train Loss: 0.2553 Acc: 0.8876 Pre: 0.8611 Recall: 0.9244 F1: 0.8916 Train AUC: 0.9613 Val AUC: 0.9615 Val PRC: 0.9642 Time: 0.71\n",
      "Epoch: 123 Train Loss: 0.2514 Acc: 0.8850 Pre: 0.8534 Recall: 0.9296 F1: 0.8899 Train AUC: 0.9620 Val AUC: 0.9629 Val PRC: 0.9641 Time: 0.71\n",
      "Epoch: 124 Train Loss: 0.2541 Acc: 0.8897 Pre: 0.8865 Recall: 0.8939 F1: 0.8902 Train AUC: 0.9609 Val AUC: 0.9604 Val PRC: 0.9625 Time: 0.71\n",
      "Epoch: 125 Train Loss: 0.2534 Acc: 0.8876 Pre: 0.9020 Recall: 0.8697 F1: 0.8856 Train AUC: 0.9616 Val AUC: 0.9609 Val PRC: 0.9629 Time: 0.72\n",
      "Epoch: 126 Train Loss: 0.2532 Acc: 0.8908 Pre: 0.8750 Recall: 0.9118 F1: 0.8930 Train AUC: 0.9613 Val AUC: 0.9615 Val PRC: 0.9631 Time: 0.72\n",
      "Epoch: 127 Train Loss: 0.2425 Acc: 0.8950 Pre: 0.9017 Recall: 0.8866 F1: 0.8941 Train AUC: 0.9651 Val AUC: 0.9640 Val PRC: 0.9652 Time: 0.71\n",
      "Epoch: 128 Train Loss: 0.2522 Acc: 0.8960 Pre: 0.9062 Recall: 0.8834 F1: 0.8947 Train AUC: 0.9612 Val AUC: 0.9641 Val PRC: 0.9666 Time: 0.71\n",
      "Epoch: 129 Train Loss: 0.2607 Acc: 0.9002 Pre: 0.9281 Recall: 0.8676 F1: 0.8969 Train AUC: 0.9587 Val AUC: 0.9643 Val PRC: 0.9669 Time: 0.71\n",
      "Epoch: 130 Train Loss: 0.2474 Acc: 0.8960 Pre: 0.8879 Recall: 0.9065 F1: 0.8971 Train AUC: 0.9629 Val AUC: 0.9657 Val PRC: 0.9669 Time: 0.72\n",
      "Epoch: 131 Train Loss: 0.2496 Acc: 0.8955 Pre: 0.8967 Recall: 0.8939 F1: 0.8953 Train AUC: 0.9625 Val AUC: 0.9629 Val PRC: 0.9641 Time: 0.98\n",
      "Epoch: 132 Train Loss: 0.2404 Acc: 0.8939 Pre: 0.8998 Recall: 0.8866 F1: 0.8931 Train AUC: 0.9652 Val AUC: 0.9635 Val PRC: 0.9642 Time: 0.71\n",
      "Epoch: 133 Train Loss: 0.2431 Acc: 0.8981 Pre: 0.8775 Recall: 0.9254 F1: 0.9008 Train AUC: 0.9639 Val AUC: 0.9671 Val PRC: 0.9683 Time: 0.73\n",
      "Epoch: 134 Train Loss: 0.2463 Acc: 0.9002 Pre: 0.9002 Recall: 0.9002 F1: 0.9002 Train AUC: 0.9633 Val AUC: 0.9628 Val PRC: 0.9638 Time: 0.73\n",
      "Epoch: 135 Train Loss: 0.2466 Acc: 0.8892 Pre: 0.8831 Recall: 0.8971 F1: 0.8900 Train AUC: 0.9633 Val AUC: 0.9625 Val PRC: 0.9628 Time: 0.72\n",
      "Epoch: 136 Train Loss: 0.2398 Acc: 0.8997 Pre: 0.8855 Recall: 0.9181 F1: 0.9015 Train AUC: 0.9657 Val AUC: 0.9652 Val PRC: 0.9661 Time: 0.70\n",
      "Epoch: 137 Train Loss: 0.2395 Acc: 0.8992 Pre: 0.9130 Recall: 0.8824 F1: 0.8974 Train AUC: 0.9653 Val AUC: 0.9661 Val PRC: 0.9677 Time: 0.73\n",
      "Epoch: 138 Train Loss: 0.2412 Acc: 0.8986 Pre: 0.8941 Recall: 0.9044 F1: 0.8992 Train AUC: 0.9646 Val AUC: 0.9655 Val PRC: 0.9650 Time: 0.71\n",
      "Epoch: 139 Train Loss: 0.2378 Acc: 0.8976 Pre: 0.8804 Recall: 0.9202 F1: 0.8998 Train AUC: 0.9655 Val AUC: 0.9646 Val PRC: 0.9641 Time: 0.71\n",
      "Epoch: 140 Train Loss: 0.2363 Acc: 0.8992 Pre: 0.9318 Recall: 0.8613 F1: 0.8952 Train AUC: 0.9664 Val AUC: 0.9658 Val PRC: 0.9665 Time: 0.74\n",
      "Epoch: 141 Train Loss: 0.2334 Acc: 0.8981 Pre: 0.9326 Recall: 0.8582 F1: 0.8939 Train AUC: 0.9678 Val AUC: 0.9654 Val PRC: 0.9658 Time: 0.73\n",
      "Epoch: 142 Train Loss: 0.2319 Acc: 0.9023 Pre: 0.9246 Recall: 0.8761 F1: 0.8997 Train AUC: 0.9673 Val AUC: 0.9665 Val PRC: 0.9619 Time: 0.70\n",
      "Epoch: 143 Train Loss: 0.2453 Acc: 0.9013 Pre: 0.9081 Recall: 0.8929 F1: 0.9004 Train AUC: 0.9634 Val AUC: 0.9672 Val PRC: 0.9660 Time: 0.74\n",
      "Epoch: 144 Train Loss: 0.2354 Acc: 0.9086 Pre: 0.9078 Recall: 0.9097 F1: 0.9087 Train AUC: 0.9663 Val AUC: 0.9680 Val PRC: 0.9651 Time: 0.71\n",
      "Epoch: 145 Train Loss: 0.2273 Acc: 0.8986 Pre: 0.8845 Recall: 0.9170 F1: 0.9005 Train AUC: 0.9685 Val AUC: 0.9670 Val PRC: 0.9623 Time: 0.71\n",
      "Epoch: 146 Train Loss: 0.2346 Acc: 0.9007 Pre: 0.9234 Recall: 0.8739 F1: 0.8980 Train AUC: 0.9666 Val AUC: 0.9666 Val PRC: 0.9674 Time: 0.77\n",
      "Epoch: 147 Train Loss: 0.2336 Acc: 0.9055 Pre: 0.9055 Recall: 0.9055 F1: 0.9055 Train AUC: 0.9674 Val AUC: 0.9676 Val PRC: 0.9661 Time: 0.70\n",
      "Epoch: 148 Train Loss: 0.2246 Acc: 0.9039 Pre: 0.9239 Recall: 0.8803 F1: 0.9016 Train AUC: 0.9693 Val AUC: 0.9680 Val PRC: 0.9635 Time: 0.70\n",
      "Epoch: 149 Train Loss: 0.2266 Acc: 0.9065 Pre: 0.9161 Recall: 0.8950 F1: 0.9054 Train AUC: 0.9692 Val AUC: 0.9689 Val PRC: 0.9689 Time: 0.72\n",
      "Epoch: 150 Train Loss: 0.2271 Acc: 0.9049 Pre: 0.9079 Recall: 0.9013 F1: 0.9046 Train AUC: 0.9685 Val AUC: 0.9667 Val PRC: 0.9658 Time: 0.70\n",
      "Epoch: 151 Train Loss: 0.2331 Acc: 0.9049 Pre: 0.8798 Recall: 0.9380 F1: 0.9080 Train AUC: 0.9670 Val AUC: 0.9679 Val PRC: 0.9693 Time: 0.71\n",
      "Epoch: 152 Train Loss: 0.2281 Acc: 0.9023 Pre: 0.9066 Recall: 0.8971 F1: 0.9018 Train AUC: 0.9686 Val AUC: 0.9682 Val PRC: 0.9685 Time: 0.72\n",
      "Epoch: 153 Train Loss: 0.2356 Acc: 0.9060 Pre: 0.9056 Recall: 0.9065 F1: 0.9060 Train AUC: 0.9667 Val AUC: 0.9671 Val PRC: 0.9681 Time: 0.72\n",
      "Epoch: 154 Train Loss: 0.2340 Acc: 0.9076 Pre: 0.9042 Recall: 0.9118 F1: 0.9079 Train AUC: 0.9667 Val AUC: 0.9673 Val PRC: 0.9685 Time: 0.71\n",
      "Epoch: 155 Train Loss: 0.2145 Acc: 0.9018 Pre: 0.8791 Recall: 0.9317 F1: 0.9046 Train AUC: 0.9720 Val AUC: 0.9700 Val PRC: 0.9707 Time: 0.73\n",
      "Epoch: 156 Train Loss: 0.2312 Acc: 0.9097 Pre: 0.9167 Recall: 0.9013 F1: 0.9089 Train AUC: 0.9677 Val AUC: 0.9689 Val PRC: 0.9683 Time: 0.70\n",
      "Epoch: 157 Train Loss: 0.2259 Acc: 0.9023 Pre: 0.9066 Recall: 0.8971 F1: 0.9018 Train AUC: 0.9694 Val AUC: 0.9673 Val PRC: 0.9611 Time: 0.71\n",
      "Epoch: 158 Train Loss: 0.2239 Acc: 0.9076 Pre: 0.9163 Recall: 0.8971 F1: 0.9066 Train AUC: 0.9697 Val AUC: 0.9689 Val PRC: 0.9627 Time: 0.71\n",
      "Epoch: 159 Train Loss: 0.2210 Acc: 0.9076 Pre: 0.9042 Recall: 0.9118 F1: 0.9079 Train AUC: 0.9704 Val AUC: 0.9705 Val PRC: 0.9704 Time: 0.70\n",
      "Epoch: 160 Train Loss: 0.2283 Acc: 0.9028 Pre: 0.8901 Recall: 0.9191 F1: 0.9044 Train AUC: 0.9685 Val AUC: 0.9693 Val PRC: 0.9663 Time: 0.72\n",
      "Epoch: 161 Train Loss: 0.2240 Acc: 0.9097 Pre: 0.9185 Recall: 0.8992 F1: 0.9087 Train AUC: 0.9690 Val AUC: 0.9695 Val PRC: 0.9664 Time: 0.72\n",
      "Epoch: 162 Train Loss: 0.2216 Acc: 0.8960 Pre: 0.8584 Recall: 0.9485 F1: 0.9012 Train AUC: 0.9700 Val AUC: 0.9697 Val PRC: 0.9696 Time: 0.72\n",
      "Epoch: 163 Train Loss: 0.2196 Acc: 0.9107 Pre: 0.9177 Recall: 0.9023 F1: 0.9100 Train AUC: 0.9705 Val AUC: 0.9715 Val PRC: 0.9719 Time: 0.70\n",
      "Epoch: 164 Train Loss: 0.2201 Acc: 0.9055 Pre: 0.8860 Recall: 0.9307 F1: 0.9078 Train AUC: 0.9704 Val AUC: 0.9724 Val PRC: 0.9713 Time: 0.73\n",
      "Epoch: 165 Train Loss: 0.2173 Acc: 0.9107 Pre: 0.8942 Recall: 0.9317 F1: 0.9126 Train AUC: 0.9713 Val AUC: 0.9739 Val PRC: 0.9744 Time: 0.71\n",
      "Epoch: 166 Train Loss: 0.2116 Acc: 0.9070 Pre: 0.9066 Recall: 0.9076 F1: 0.9071 Train AUC: 0.9731 Val AUC: 0.9709 Val PRC: 0.9700 Time: 0.72\n",
      "Epoch: 167 Train Loss: 0.2090 Acc: 0.9144 Pre: 0.9013 Recall: 0.9307 F1: 0.9158 Train AUC: 0.9732 Val AUC: 0.9723 Val PRC: 0.9723 Time: 0.73\n",
      "Epoch: 168 Train Loss: 0.2140 Acc: 0.9086 Pre: 0.9019 Recall: 0.9170 F1: 0.9094 Train AUC: 0.9720 Val AUC: 0.9705 Val PRC: 0.9696 Time: 0.73\n",
      "Epoch: 169 Train Loss: 0.2104 Acc: 0.9076 Pre: 0.9067 Recall: 0.9086 F1: 0.9077 Train AUC: 0.9725 Val AUC: 0.9685 Val PRC: 0.9681 Time: 0.72\n",
      "Epoch: 170 Train Loss: 0.2125 Acc: 0.9149 Pre: 0.8998 Recall: 0.9338 F1: 0.9165 Train AUC: 0.9721 Val AUC: 0.9730 Val PRC: 0.9678 Time: 0.73\n",
      "Epoch: 171 Train Loss: 0.2154 Acc: 0.9091 Pre: 0.9238 Recall: 0.8918 F1: 0.9075 Train AUC: 0.9715 Val AUC: 0.9708 Val PRC: 0.9705 Time: 0.72\n",
      "Epoch: 172 Train Loss: 0.2128 Acc: 0.9097 Pre: 0.8861 Recall: 0.9401 F1: 0.9123 Train AUC: 0.9720 Val AUC: 0.9708 Val PRC: 0.9704 Time: 0.72\n",
      "Epoch: 173 Train Loss: 0.2088 Acc: 0.9128 Pre: 0.9244 Recall: 0.8992 F1: 0.9116 Train AUC: 0.9729 Val AUC: 0.9718 Val PRC: 0.9702 Time: 0.71\n",
      "Epoch: 174 Train Loss: 0.2207 Acc: 0.9149 Pre: 0.9193 Recall: 0.9097 F1: 0.9145 Train AUC: 0.9704 Val AUC: 0.9715 Val PRC: 0.9698 Time: 0.74\n",
      "Epoch: 175 Train Loss: 0.2150 Acc: 0.9149 Pre: 0.9149 Recall: 0.9149 F1: 0.9149 Train AUC: 0.9711 Val AUC: 0.9707 Val PRC: 0.9649 Time: 0.71\n",
      "Epoch: 176 Train Loss: 0.2057 Acc: 0.9091 Pre: 0.9096 Recall: 0.9086 F1: 0.9091 Train AUC: 0.9736 Val AUC: 0.9731 Val PRC: 0.9720 Time: 0.70\n",
      "Epoch: 177 Train Loss: 0.2038 Acc: 0.9081 Pre: 0.9094 Recall: 0.9065 F1: 0.9079 Train AUC: 0.9746 Val AUC: 0.9721 Val PRC: 0.9707 Time: 0.73\n",
      "Epoch: 178 Train Loss: 0.2090 Acc: 0.9112 Pre: 0.8935 Recall: 0.9338 F1: 0.9132 Train AUC: 0.9730 Val AUC: 0.9733 Val PRC: 0.9688 Time: 0.72\n",
      "Epoch: 179 Train Loss: 0.2096 Acc: 0.9144 Pre: 0.9321 Recall: 0.8939 F1: 0.9126 Train AUC: 0.9729 Val AUC: 0.9751 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 180 Train Loss: 0.2026 Acc: 0.9112 Pre: 0.9007 Recall: 0.9244 F1: 0.9124 Train AUC: 0.9750 Val AUC: 0.9748 Val PRC: 0.9746 Time: 0.72\n",
      "Epoch: 181 Train Loss: 0.2045 Acc: 0.9170 Pre: 0.9144 Recall: 0.9202 F1: 0.9173 Train AUC: 0.9741 Val AUC: 0.9744 Val PRC: 0.9749 Time: 0.72\n",
      "Epoch: 182 Train Loss: 0.1985 Acc: 0.9154 Pre: 0.9048 Recall: 0.9286 F1: 0.9165 Train AUC: 0.9762 Val AUC: 0.9738 Val PRC: 0.9729 Time: 0.71\n",
      "Epoch: 183 Train Loss: 0.2031 Acc: 0.9128 Pre: 0.9043 Recall: 0.9233 F1: 0.9137 Train AUC: 0.9744 Val AUC: 0.9742 Val PRC: 0.9727 Time: 0.71\n",
      "Epoch: 184 Train Loss: 0.1997 Acc: 0.9097 Pre: 0.8980 Recall: 0.9244 F1: 0.9110 Train AUC: 0.9753 Val AUC: 0.9705 Val PRC: 0.9705 Time: 0.70\n",
      "Epoch: 185 Train Loss: 0.2022 Acc: 0.9112 Pre: 0.8975 Recall: 0.9286 F1: 0.9128 Train AUC: 0.9745 Val AUC: 0.9726 Val PRC: 0.9726 Time: 0.70\n",
      "Epoch: 186 Train Loss: 0.1949 Acc: 0.9070 Pre: 0.8974 Recall: 0.9191 F1: 0.9081 Train AUC: 0.9763 Val AUC: 0.9721 Val PRC: 0.9715 Time: 0.72\n",
      "Epoch: 187 Train Loss: 0.1967 Acc: 0.9112 Pre: 0.9143 Recall: 0.9076 F1: 0.9109 Train AUC: 0.9762 Val AUC: 0.9733 Val PRC: 0.9678 Time: 0.71\n",
      "Epoch: 188 Train Loss: 0.2032 Acc: 0.9112 Pre: 0.9143 Recall: 0.9076 F1: 0.9109 Train AUC: 0.9741 Val AUC: 0.9725 Val PRC: 0.9717 Time: 0.72\n",
      "Epoch: 189 Train Loss: 0.1974 Acc: 0.9170 Pre: 0.9315 Recall: 0.9002 F1: 0.9156 Train AUC: 0.9758 Val AUC: 0.9744 Val PRC: 0.9732 Time: 0.72\n",
      "Epoch: 190 Train Loss: 0.1950 Acc: 0.9123 Pre: 0.9076 Recall: 0.9181 F1: 0.9128 Train AUC: 0.9767 Val AUC: 0.9730 Val PRC: 0.9729 Time: 0.71\n",
      "Epoch: 191 Train Loss: 0.1969 Acc: 0.9107 Pre: 0.8998 Recall: 0.9244 F1: 0.9119 Train AUC: 0.9761 Val AUC: 0.9727 Val PRC: 0.9711 Time: 0.72\n",
      "Epoch: 192 Train Loss: 0.1948 Acc: 0.9170 Pre: 0.9101 Recall: 0.9254 F1: 0.9177 Train AUC: 0.9762 Val AUC: 0.9744 Val PRC: 0.9735 Time: 0.74\n",
      "Epoch: 193 Train Loss: 0.1899 Acc: 0.9212 Pre: 0.9134 Recall: 0.9307 F1: 0.9220 Train AUC: 0.9782 Val AUC: 0.9756 Val PRC: 0.9759 Time: 0.72\n",
      "Epoch: 194 Train Loss: 0.1946 Acc: 0.9191 Pre: 0.9130 Recall: 0.9265 F1: 0.9197 Train AUC: 0.9762 Val AUC: 0.9751 Val PRC: 0.9746 Time: 0.72\n",
      "Epoch: 195 Train Loss: 0.1986 Acc: 0.9144 Pre: 0.9166 Recall: 0.9118 F1: 0.9142 Train AUC: 0.9760 Val AUC: 0.9737 Val PRC: 0.9738 Time: 0.72\n",
      "Epoch: 196 Train Loss: 0.1844 Acc: 0.9238 Pre: 0.9208 Recall: 0.9275 F1: 0.9241 Train AUC: 0.9789 Val AUC: 0.9767 Val PRC: 0.9757 Time: 0.71\n",
      "Epoch: 197 Train Loss: 0.1968 Acc: 0.9154 Pre: 0.9065 Recall: 0.9265 F1: 0.9164 Train AUC: 0.9757 Val AUC: 0.9741 Val PRC: 0.9727 Time: 0.71\n",
      "Epoch: 198 Train Loss: 0.1914 Acc: 0.9202 Pre: 0.9115 Recall: 0.9307 F1: 0.9210 Train AUC: 0.9767 Val AUC: 0.9741 Val PRC: 0.9666 Time: 0.71\n",
      "Epoch: 199 Train Loss: 0.1876 Acc: 0.9165 Pre: 0.9025 Recall: 0.9338 F1: 0.9179 Train AUC: 0.9777 Val AUC: 0.9754 Val PRC: 0.9712 Time: 0.73\n",
      "Epoch: 200 Train Loss: 0.1919 Acc: 0.9154 Pre: 0.9176 Recall: 0.9128 F1: 0.9152 Train AUC: 0.9776 Val AUC: 0.9754 Val PRC: 0.9749 Time: 0.73\n",
      "Epoch: 201 Train Loss: 0.1829 Acc: 0.9170 Pre: 0.9043 Recall: 0.9328 F1: 0.9183 Train AUC: 0.9789 Val AUC: 0.9740 Val PRC: 0.9737 Time: 0.71\n",
      "Epoch: 202 Train Loss: 0.1929 Acc: 0.9160 Pre: 0.9222 Recall: 0.9086 F1: 0.9153 Train AUC: 0.9775 Val AUC: 0.9762 Val PRC: 0.9744 Time: 0.72\n",
      "Epoch: 203 Train Loss: 0.1900 Acc: 0.9181 Pre: 0.8980 Recall: 0.9433 F1: 0.9201 Train AUC: 0.9777 Val AUC: 0.9762 Val PRC: 0.9765 Time: 0.74\n",
      "Epoch: 204 Train Loss: 0.1834 Acc: 0.9144 Pre: 0.9080 Recall: 0.9223 F1: 0.9151 Train AUC: 0.9794 Val AUC: 0.9748 Val PRC: 0.9755 Time: 0.70\n",
      "Epoch: 205 Train Loss: 0.1952 Acc: 0.9191 Pre: 0.9063 Recall: 0.9349 F1: 0.9204 Train AUC: 0.9773 Val AUC: 0.9756 Val PRC: 0.9762 Time: 0.71\n",
      "Epoch: 206 Train Loss: 0.1831 Acc: 0.9175 Pre: 0.9077 Recall: 0.9296 F1: 0.9185 Train AUC: 0.9790 Val AUC: 0.9759 Val PRC: 0.9761 Time: 0.70\n",
      "Epoch: 207 Train Loss: 0.1890 Acc: 0.9181 Pre: 0.9252 Recall: 0.9097 F1: 0.9174 Train AUC: 0.9776 Val AUC: 0.9754 Val PRC: 0.9741 Time: 0.71\n",
      "Epoch: 208 Train Loss: 0.1883 Acc: 0.9212 Pre: 0.9126 Recall: 0.9317 F1: 0.9220 Train AUC: 0.9778 Val AUC: 0.9766 Val PRC: 0.9742 Time: 0.72\n",
      "Epoch: 209 Train Loss: 0.1801 Acc: 0.9118 Pre: 0.9016 Recall: 0.9244 F1: 0.9129 Train AUC: 0.9798 Val AUC: 0.9735 Val PRC: 0.9711 Time: 0.71\n",
      "Epoch: 210 Train Loss: 0.1866 Acc: 0.9160 Pre: 0.9125 Recall: 0.9202 F1: 0.9163 Train AUC: 0.9782 Val AUC: 0.9751 Val PRC: 0.9746 Time: 0.71\n",
      "Epoch: 211 Train Loss: 0.1794 Acc: 0.9191 Pre: 0.9182 Recall: 0.9202 F1: 0.9192 Train AUC: 0.9801 Val AUC: 0.9750 Val PRC: 0.9745 Time: 0.72\n",
      "Epoch: 212 Train Loss: 0.1860 Acc: 0.9170 Pre: 0.9043 Recall: 0.9328 F1: 0.9183 Train AUC: 0.9775 Val AUC: 0.9739 Val PRC: 0.9724 Time: 0.71\n",
      "Epoch: 213 Train Loss: 0.1810 Acc: 0.9196 Pre: 0.9175 Recall: 0.9223 F1: 0.9199 Train AUC: 0.9794 Val AUC: 0.9747 Val PRC: 0.9745 Time: 0.70\n",
      "Epoch: 214 Train Loss: 0.1837 Acc: 0.9191 Pre: 0.9063 Recall: 0.9349 F1: 0.9204 Train AUC: 0.9789 Val AUC: 0.9759 Val PRC: 0.9755 Time: 0.76\n",
      "Epoch: 215 Train Loss: 0.1749 Acc: 0.9191 Pre: 0.8974 Recall: 0.9464 F1: 0.9213 Train AUC: 0.9809 Val AUC: 0.9757 Val PRC: 0.9757 Time: 0.72\n",
      "Epoch: 216 Train Loss: 0.1739 Acc: 0.9217 Pre: 0.9043 Recall: 0.9433 F1: 0.9234 Train AUC: 0.9810 Val AUC: 0.9766 Val PRC: 0.9763 Time: 0.72\n",
      "Epoch: 217 Train Loss: 0.1721 Acc: 0.9154 Pre: 0.8975 Recall: 0.9380 F1: 0.9173 Train AUC: 0.9814 Val AUC: 0.9746 Val PRC: 0.9757 Time: 0.72\n",
      "Epoch: 218 Train Loss: 0.1837 Acc: 0.9154 Pre: 0.9203 Recall: 0.9097 F1: 0.9149 Train AUC: 0.9785 Val AUC: 0.9749 Val PRC: 0.9741 Time: 0.71\n",
      "Epoch: 219 Train Loss: 0.1727 Acc: 0.9186 Pre: 0.9147 Recall: 0.9233 F1: 0.9190 Train AUC: 0.9814 Val AUC: 0.9766 Val PRC: 0.9760 Time: 0.70\n",
      "Epoch: 220 Train Loss: 0.1738 Acc: 0.9196 Pre: 0.9157 Recall: 0.9244 F1: 0.9200 Train AUC: 0.9805 Val AUC: 0.9766 Val PRC: 0.9745 Time: 0.77\n",
      "Epoch: 221 Train Loss: 0.1755 Acc: 0.9259 Pre: 0.9067 Recall: 0.9496 F1: 0.9277 Train AUC: 0.9799 Val AUC: 0.9781 Val PRC: 0.9764 Time: 0.71\n",
      "Epoch: 222 Train Loss: 0.1816 Acc: 0.9238 Pre: 0.9164 Recall: 0.9328 F1: 0.9245 Train AUC: 0.9785 Val AUC: 0.9777 Val PRC: 0.9779 Time: 0.70\n",
      "Epoch: 223 Train Loss: 0.1767 Acc: 0.9186 Pre: 0.9046 Recall: 0.9359 F1: 0.9200 Train AUC: 0.9796 Val AUC: 0.9753 Val PRC: 0.9742 Time: 0.75\n",
      "Epoch: 224 Train Loss: 0.1719 Acc: 0.9186 Pre: 0.9096 Recall: 0.9296 F1: 0.9195 Train AUC: 0.9806 Val AUC: 0.9745 Val PRC: 0.9747 Time: 0.71\n",
      "Epoch: 225 Train Loss: 0.1648 Acc: 0.9249 Pre: 0.9253 Recall: 0.9244 F1: 0.9249 Train AUC: 0.9826 Val AUC: 0.9778 Val PRC: 0.9795 Time: 0.71\n",
      "Epoch: 226 Train Loss: 0.1734 Acc: 0.9270 Pre: 0.9395 Recall: 0.9128 F1: 0.9259 Train AUC: 0.9814 Val AUC: 0.9765 Val PRC: 0.9726 Time: 0.71\n",
      "Epoch: 227 Train Loss: 0.1690 Acc: 0.9223 Pre: 0.9379 Recall: 0.9044 F1: 0.9209 Train AUC: 0.9818 Val AUC: 0.9769 Val PRC: 0.9767 Time: 0.72\n",
      "Epoch: 228 Train Loss: 0.1772 Acc: 0.9212 Pre: 0.9203 Recall: 0.9223 F1: 0.9213 Train AUC: 0.9807 Val AUC: 0.9765 Val PRC: 0.9754 Time: 0.71\n",
      "Epoch: 229 Train Loss: 0.1644 Acc: 0.9233 Pre: 0.9343 Recall: 0.9107 F1: 0.9223 Train AUC: 0.9829 Val AUC: 0.9764 Val PRC: 0.9775 Time: 0.73\n",
      "Epoch: 230 Train Loss: 0.1753 Acc: 0.9217 Pre: 0.9204 Recall: 0.9233 F1: 0.9219 Train AUC: 0.9805 Val AUC: 0.9776 Val PRC: 0.9779 Time: 0.74\n",
      "Epoch: 231 Train Loss: 0.1696 Acc: 0.9244 Pre: 0.9226 Recall: 0.9265 F1: 0.9245 Train AUC: 0.9821 Val AUC: 0.9761 Val PRC: 0.9774 Time: 0.73\n",
      "Epoch: 232 Train Loss: 0.1751 Acc: 0.9160 Pre: 0.8952 Recall: 0.9422 F1: 0.9181 Train AUC: 0.9805 Val AUC: 0.9767 Val PRC: 0.9781 Time: 0.71\n",
      "Epoch: 233 Train Loss: 0.1691 Acc: 0.9233 Pre: 0.9121 Recall: 0.9370 F1: 0.9244 Train AUC: 0.9818 Val AUC: 0.9776 Val PRC: 0.9790 Time: 0.73\n",
      "Epoch: 234 Train Loss: 0.1562 Acc: 0.9202 Pre: 0.9082 Recall: 0.9349 F1: 0.9213 Train AUC: 0.9848 Val AUC: 0.9788 Val PRC: 0.9793 Time: 0.73\n",
      "Epoch: 235 Train Loss: 0.1641 Acc: 0.9223 Pre: 0.9136 Recall: 0.9328 F1: 0.9231 Train AUC: 0.9822 Val AUC: 0.9775 Val PRC: 0.9784 Time: 0.72\n",
      "Epoch: 236 Train Loss: 0.1672 Acc: 0.9212 Pre: 0.9151 Recall: 0.9286 F1: 0.9218 Train AUC: 0.9815 Val AUC: 0.9769 Val PRC: 0.9766 Time: 0.70\n",
      "Epoch: 237 Train Loss: 0.1642 Acc: 0.9191 Pre: 0.9071 Recall: 0.9338 F1: 0.9203 Train AUC: 0.9827 Val AUC: 0.9773 Val PRC: 0.9772 Time: 0.72\n",
      "Epoch: 238 Train Loss: 0.1655 Acc: 0.9228 Pre: 0.9070 Recall: 0.9422 F1: 0.9243 Train AUC: 0.9825 Val AUC: 0.9772 Val PRC: 0.9787 Time: 0.72\n",
      "Epoch: 239 Train Loss: 0.1662 Acc: 0.9191 Pre: 0.9272 Recall: 0.9097 F1: 0.9183 Train AUC: 0.9825 Val AUC: 0.9764 Val PRC: 0.9766 Time: 0.71\n",
      "Epoch: 240 Train Loss: 0.1671 Acc: 0.9280 Pre: 0.9386 Recall: 0.9160 F1: 0.9272 Train AUC: 0.9817 Val AUC: 0.9762 Val PRC: 0.9776 Time: 0.70\n",
      "Epoch: 241 Train Loss: 0.1646 Acc: 0.9238 Pre: 0.9429 Recall: 0.9023 F1: 0.9222 Train AUC: 0.9826 Val AUC: 0.9764 Val PRC: 0.9769 Time: 0.70\n",
      "Epoch: 242 Train Loss: 0.1596 Acc: 0.9228 Pre: 0.9095 Recall: 0.9391 F1: 0.9240 Train AUC: 0.9837 Val AUC: 0.9759 Val PRC: 0.9777 Time: 0.72\n",
      "Epoch: 243 Train Loss: 0.1630 Acc: 0.9202 Pre: 0.9073 Recall: 0.9359 F1: 0.9214 Train AUC: 0.9829 Val AUC: 0.9754 Val PRC: 0.9761 Time: 0.70\n",
      "Epoch: 244 Train Loss: 0.1634 Acc: 0.9228 Pre: 0.9180 Recall: 0.9286 F1: 0.9232 Train AUC: 0.9826 Val AUC: 0.9784 Val PRC: 0.9795 Time: 0.73\n",
      "Epoch: 245 Train Loss: 0.1598 Acc: 0.9301 Pre: 0.9352 Recall: 0.9244 F1: 0.9297 Train AUC: 0.9837 Val AUC: 0.9789 Val PRC: 0.9785 Time: 0.71\n",
      "Epoch: 246 Train Loss: 0.1606 Acc: 0.9212 Pre: 0.9059 Recall: 0.9401 F1: 0.9227 Train AUC: 0.9829 Val AUC: 0.9771 Val PRC: 0.9766 Time: 0.70\n",
      "Epoch: 247 Train Loss: 0.1638 Acc: 0.9296 Pre: 0.9234 Recall: 0.9370 F1: 0.9301 Train AUC: 0.9821 Val AUC: 0.9763 Val PRC: 0.9735 Time: 0.70\n",
      "Epoch: 248 Train Loss: 0.1636 Acc: 0.9207 Pre: 0.9116 Recall: 0.9317 F1: 0.9216 Train AUC: 0.9814 Val AUC: 0.9775 Val PRC: 0.9751 Time: 0.72\n",
      "Epoch: 249 Train Loss: 0.1580 Acc: 0.9265 Pre: 0.9247 Recall: 0.9286 F1: 0.9266 Train AUC: 0.9836 Val AUC: 0.9765 Val PRC: 0.9717 Time: 0.71\n",
      "Epoch: 250 Train Loss: 0.1559 Acc: 0.9296 Pre: 0.9351 Recall: 0.9233 F1: 0.9292 Train AUC: 0.9849 Val AUC: 0.9783 Val PRC: 0.9776 Time: 0.71\n",
      "Epoch: 251 Train Loss: 0.1614 Acc: 0.9270 Pre: 0.9221 Recall: 0.9328 F1: 0.9274 Train AUC: 0.9829 Val AUC: 0.9768 Val PRC: 0.9785 Time: 0.74\n",
      "Epoch: 252 Train Loss: 0.1524 Acc: 0.9259 Pre: 0.9193 Recall: 0.9338 F1: 0.9265 Train AUC: 0.9841 Val AUC: 0.9771 Val PRC: 0.9761 Time: 0.73\n",
      "Epoch: 253 Train Loss: 0.1570 Acc: 0.9244 Pre: 0.9191 Recall: 0.9307 F1: 0.9248 Train AUC: 0.9832 Val AUC: 0.9774 Val PRC: 0.9776 Time: 0.73\n",
      "Epoch: 254 Train Loss: 0.1598 Acc: 0.9223 Pre: 0.9119 Recall: 0.9349 F1: 0.9232 Train AUC: 0.9829 Val AUC: 0.9779 Val PRC: 0.9787 Time: 0.72\n",
      "Epoch: 255 Train Loss: 0.1526 Acc: 0.9238 Pre: 0.9297 Recall: 0.9170 F1: 0.9233 Train AUC: 0.9845 Val AUC: 0.9761 Val PRC: 0.9775 Time: 0.71\n",
      "Epoch: 256 Train Loss: 0.1534 Acc: 0.9254 Pre: 0.9228 Recall: 0.9286 F1: 0.9257 Train AUC: 0.9848 Val AUC: 0.9781 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 257 Train Loss: 0.1513 Acc: 0.9249 Pre: 0.9271 Recall: 0.9223 F1: 0.9247 Train AUC: 0.9846 Val AUC: 0.9770 Val PRC: 0.9774 Time: 0.72\n",
      "Epoch: 258 Train Loss: 0.1620 Acc: 0.9254 Pre: 0.9184 Recall: 0.9338 F1: 0.9260 Train AUC: 0.9818 Val AUC: 0.9761 Val PRC: 0.9771 Time: 0.74\n",
      "Epoch: 259 Train Loss: 0.1518 Acc: 0.9249 Pre: 0.9140 Recall: 0.9380 F1: 0.9259 Train AUC: 0.9850 Val AUC: 0.9789 Val PRC: 0.9799 Time: 0.73\n",
      "Epoch: 260 Train Loss: 0.1558 Acc: 0.9244 Pre: 0.9391 Recall: 0.9076 F1: 0.9231 Train AUC: 0.9851 Val AUC: 0.9753 Val PRC: 0.9756 Time: 0.71\n",
      "Epoch: 261 Train Loss: 0.1547 Acc: 0.9223 Pre: 0.9061 Recall: 0.9422 F1: 0.9238 Train AUC: 0.9841 Val AUC: 0.9766 Val PRC: 0.9777 Time: 0.72\n",
      "Epoch: 262 Train Loss: 0.1656 Acc: 0.9244 Pre: 0.9289 Recall: 0.9191 F1: 0.9240 Train AUC: 0.9821 Val AUC: 0.9773 Val PRC: 0.9790 Time: 0.73\n",
      "Epoch: 263 Train Loss: 0.1567 Acc: 0.9254 Pre: 0.9490 Recall: 0.8992 F1: 0.9234 Train AUC: 0.9834 Val AUC: 0.9771 Val PRC: 0.9770 Time: 0.73\n",
      "Epoch: 264 Train Loss: 0.1493 Acc: 0.9196 Pre: 0.8975 Recall: 0.9475 F1: 0.9218 Train AUC: 0.9858 Val AUC: 0.9761 Val PRC: 0.9770 Time: 1.11\n",
      "Epoch: 265 Train Loss: 0.1494 Acc: 0.9291 Pre: 0.9305 Recall: 0.9275 F1: 0.9290 Train AUC: 0.9844 Val AUC: 0.9774 Val PRC: 0.9777 Time: 0.73\n",
      "Epoch: 266 Train Loss: 0.1631 Acc: 0.9249 Pre: 0.9166 Recall: 0.9349 F1: 0.9256 Train AUC: 0.9827 Val AUC: 0.9778 Val PRC: 0.9798 Time: 0.71\n",
      "Epoch: 267 Train Loss: 0.1567 Acc: 0.9296 Pre: 0.9234 Recall: 0.9370 F1: 0.9301 Train AUC: 0.9833 Val AUC: 0.9795 Val PRC: 0.9811 Time: 0.72\n",
      "Epoch: 268 Train Loss: 0.1502 Acc: 0.9275 Pre: 0.9367 Recall: 0.9170 F1: 0.9268 Train AUC: 0.9849 Val AUC: 0.9773 Val PRC: 0.9785 Time: 0.71\n",
      "Epoch: 269 Train Loss: 0.1508 Acc: 0.9291 Pre: 0.9190 Recall: 0.9412 F1: 0.9299 Train AUC: 0.9847 Val AUC: 0.9779 Val PRC: 0.9757 Time: 0.72\n",
      "Epoch: 270 Train Loss: 0.1544 Acc: 0.9259 Pre: 0.9125 Recall: 0.9422 F1: 0.9271 Train AUC: 0.9833 Val AUC: 0.9770 Val PRC: 0.9738 Time: 0.71\n",
      "Epoch: 271 Train Loss: 0.1413 Acc: 0.9291 Pre: 0.9305 Recall: 0.9275 F1: 0.9290 Train AUC: 0.9867 Val AUC: 0.9786 Val PRC: 0.9780 Time: 0.73\n",
      "Epoch: 272 Train Loss: 0.1552 Acc: 0.9238 Pre: 0.9181 Recall: 0.9307 F1: 0.9244 Train AUC: 0.9837 Val AUC: 0.9765 Val PRC: 0.9769 Time: 0.73\n",
      "Epoch: 273 Train Loss: 0.1566 Acc: 0.9280 Pre: 0.9368 Recall: 0.9181 F1: 0.9273 Train AUC: 0.9832 Val AUC: 0.9791 Val PRC: 0.9786 Time: 0.71\n",
      "Epoch: 274 Train Loss: 0.1357 Acc: 0.9275 Pre: 0.9145 Recall: 0.9433 F1: 0.9286 Train AUC: 0.9881 Val AUC: 0.9792 Val PRC: 0.9802 Time: 0.71\n",
      "Epoch: 275 Train Loss: 0.1377 Acc: 0.9280 Pre: 0.9405 Recall: 0.9139 F1: 0.9270 Train AUC: 0.9871 Val AUC: 0.9781 Val PRC: 0.9785 Time: 0.73\n",
      "Epoch: 276 Train Loss: 0.1496 Acc: 0.9296 Pre: 0.9252 Recall: 0.9349 F1: 0.9300 Train AUC: 0.9843 Val AUC: 0.9790 Val PRC: 0.9803 Time: 0.71\n",
      "Epoch: 277 Train Loss: 0.1468 Acc: 0.9270 Pre: 0.9135 Recall: 0.9433 F1: 0.9282 Train AUC: 0.9858 Val AUC: 0.9782 Val PRC: 0.9794 Time: 0.70\n",
      "Epoch: 278 Train Loss: 0.1439 Acc: 0.9280 Pre: 0.9396 Recall: 0.9149 F1: 0.9271 Train AUC: 0.9864 Val AUC: 0.9791 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 279 Train Loss: 0.1413 Acc: 0.9270 Pre: 0.9195 Recall: 0.9359 F1: 0.9276 Train AUC: 0.9864 Val AUC: 0.9770 Val PRC: 0.9785 Time: 0.71\n",
      "Epoch: 280 Train Loss: 0.1432 Acc: 0.9286 Pre: 0.9286 Recall: 0.9286 F1: 0.9286 Train AUC: 0.9858 Val AUC: 0.9789 Val PRC: 0.9805 Time: 0.71\n",
      "Epoch: 281 Train Loss: 0.1610 Acc: 0.9286 Pre: 0.9172 Recall: 0.9422 F1: 0.9295 Train AUC: 0.9848 Val AUC: 0.9773 Val PRC: 0.9773 Time: 0.73\n",
      "Epoch: 282 Train Loss: 0.1645 Acc: 0.9307 Pre: 0.9244 Recall: 0.9380 F1: 0.9312 Train AUC: 0.9844 Val AUC: 0.9767 Val PRC: 0.9782 Time: 0.72\n",
      "Epoch: 283 Train Loss: 0.1429 Acc: 0.9338 Pre: 0.9311 Recall: 0.9370 F1: 0.9340 Train AUC: 0.9860 Val AUC: 0.9771 Val PRC: 0.9772 Time: 0.71\n",
      "Epoch: 284 Train Loss: 0.1473 Acc: 0.9275 Pre: 0.9222 Recall: 0.9338 F1: 0.9280 Train AUC: 0.9858 Val AUC: 0.9766 Val PRC: 0.9715 Time: 0.71\n",
      "Epoch: 285 Train Loss: 0.1421 Acc: 0.9296 Pre: 0.9475 Recall: 0.9097 F1: 0.9282 Train AUC: 0.9863 Val AUC: 0.9776 Val PRC: 0.9767 Time: 0.72\n",
      "Epoch: 286 Train Loss: 0.1416 Acc: 0.9296 Pre: 0.9208 Recall: 0.9401 F1: 0.9304 Train AUC: 0.9877 Val AUC: 0.9760 Val PRC: 0.9718 Time: 0.73\n",
      "Epoch: 287 Train Loss: 0.1447 Acc: 0.9296 Pre: 0.9278 Recall: 0.9317 F1: 0.9298 Train AUC: 0.9853 Val AUC: 0.9771 Val PRC: 0.9771 Time: 0.39\n",
      "Epoch: 288 Train Loss: 0.1489 Acc: 0.9338 Pre: 0.9357 Recall: 0.9317 F1: 0.9337 Train AUC: 0.9847 Val AUC: 0.9776 Val PRC: 0.9798 Time: 0.74\n",
      "Epoch: 289 Train Loss: 0.1383 Acc: 0.9354 Pre: 0.9510 Recall: 0.9181 F1: 0.9343 Train AUC: 0.9863 Val AUC: 0.9783 Val PRC: 0.9744 Time: 0.72\n",
      "Epoch: 290 Train Loss: 0.1460 Acc: 0.9312 Pre: 0.9496 Recall: 0.9107 F1: 0.9298 Train AUC: 0.9852 Val AUC: 0.9775 Val PRC: 0.9782 Time: 0.72\n",
      "Epoch: 291 Train Loss: 0.1405 Acc: 0.9270 Pre: 0.9348 Recall: 0.9181 F1: 0.9263 Train AUC: 0.9868 Val AUC: 0.9756 Val PRC: 0.9757 Time: 0.72\n",
      "Epoch: 292 Train Loss: 0.1396 Acc: 0.9280 Pre: 0.9154 Recall: 0.9433 F1: 0.9291 Train AUC: 0.9870 Val AUC: 0.9767 Val PRC: 0.9774 Time: 0.72\n",
      "Epoch: 293 Train Loss: 0.1457 Acc: 0.9286 Pre: 0.9215 Recall: 0.9370 F1: 0.9292 Train AUC: 0.9855 Val AUC: 0.9778 Val PRC: 0.9790 Time: 0.72\n",
      "Epoch: 294 Train Loss: 0.1358 Acc: 0.9265 Pre: 0.9134 Recall: 0.9422 F1: 0.9276 Train AUC: 0.9873 Val AUC: 0.9790 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 295 Train Loss: 0.1337 Acc: 0.9291 Pre: 0.9181 Recall: 0.9422 F1: 0.9300 Train AUC: 0.9883 Val AUC: 0.9797 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 296 Train Loss: 0.1417 Acc: 0.9291 Pre: 0.9295 Recall: 0.9286 F1: 0.9291 Train AUC: 0.9856 Val AUC: 0.9774 Val PRC: 0.9771 Time: 0.71\n",
      "Epoch: 297 Train Loss: 0.1356 Acc: 0.9307 Pre: 0.9227 Recall: 0.9401 F1: 0.9313 Train AUC: 0.9874 Val AUC: 0.9786 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 298 Train Loss: 0.1342 Acc: 0.9286 Pre: 0.9295 Recall: 0.9275 F1: 0.9285 Train AUC: 0.9876 Val AUC: 0.9772 Val PRC: 0.9794 Time: 0.71\n",
      "Epoch: 299 Train Loss: 0.1315 Acc: 0.9286 Pre: 0.9155 Recall: 0.9443 F1: 0.9297 Train AUC: 0.9878 Val AUC: 0.9787 Val PRC: 0.9792 Time: 0.72\n",
      "Epoch: 300 Train Loss: 0.1318 Acc: 0.9280 Pre: 0.9223 Recall: 0.9349 F1: 0.9285 Train AUC: 0.9884 Val AUC: 0.9775 Val PRC: 0.9793 Time: 0.72\n",
      "Epoch: 301 Train Loss: 0.1322 Acc: 0.9254 Pre: 0.9460 Recall: 0.9023 F1: 0.9237 Train AUC: 0.9878 Val AUC: 0.9775 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 302 Train Loss: 0.1375 Acc: 0.9312 Pre: 0.9477 Recall: 0.9128 F1: 0.9299 Train AUC: 0.9864 Val AUC: 0.9787 Val PRC: 0.9809 Time: 0.71\n",
      "Epoch: 303 Train Loss: 0.1335 Acc: 0.9270 Pre: 0.9257 Recall: 0.9286 F1: 0.9271 Train AUC: 0.9877 Val AUC: 0.9783 Val PRC: 0.9784 Time: 0.71\n",
      "Epoch: 304 Train Loss: 0.1400 Acc: 0.9280 Pre: 0.9386 Recall: 0.9160 F1: 0.9272 Train AUC: 0.9860 Val AUC: 0.9789 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 305 Train Loss: 0.1337 Acc: 0.9322 Pre: 0.9264 Recall: 0.9391 F1: 0.9327 Train AUC: 0.9873 Val AUC: 0.9789 Val PRC: 0.9808 Time: 0.70\n",
      "Epoch: 306 Train Loss: 0.1371 Acc: 0.9280 Pre: 0.9214 Recall: 0.9359 F1: 0.9286 Train AUC: 0.9865 Val AUC: 0.9794 Val PRC: 0.9811 Time: 0.71\n",
      "Epoch: 307 Train Loss: 0.1402 Acc: 0.9296 Pre: 0.9140 Recall: 0.9485 F1: 0.9309 Train AUC: 0.9864 Val AUC: 0.9795 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 308 Train Loss: 0.1309 Acc: 0.9307 Pre: 0.9244 Recall: 0.9380 F1: 0.9312 Train AUC: 0.9884 Val AUC: 0.9779 Val PRC: 0.9790 Time: 0.70\n",
      "Epoch: 309 Train Loss: 0.1365 Acc: 0.9275 Pre: 0.9145 Recall: 0.9433 F1: 0.9286 Train AUC: 0.9865 Val AUC: 0.9777 Val PRC: 0.9785 Time: 0.73\n",
      "Epoch: 310 Train Loss: 0.1326 Acc: 0.9312 Pre: 0.9307 Recall: 0.9317 F1: 0.9312 Train AUC: 0.9878 Val AUC: 0.9792 Val PRC: 0.9740 Time: 0.70\n",
      "Epoch: 311 Train Loss: 0.1334 Acc: 0.9312 Pre: 0.9381 Recall: 0.9233 F1: 0.9307 Train AUC: 0.9881 Val AUC: 0.9794 Val PRC: 0.9787 Time: 0.73\n",
      "Epoch: 312 Train Loss: 0.1278 Acc: 0.9349 Pre: 0.9481 Recall: 0.9202 F1: 0.9339 Train AUC: 0.9886 Val AUC: 0.9772 Val PRC: 0.9776 Time: 0.74\n",
      "Epoch: 313 Train Loss: 0.1390 Acc: 0.9301 Pre: 0.9252 Recall: 0.9359 F1: 0.9305 Train AUC: 0.9863 Val AUC: 0.9780 Val PRC: 0.9786 Time: 0.72\n",
      "Epoch: 314 Train Loss: 0.1324 Acc: 0.9354 Pre: 0.9396 Recall: 0.9307 F1: 0.9351 Train AUC: 0.9876 Val AUC: 0.9793 Val PRC: 0.9754 Time: 0.70\n",
      "Epoch: 315 Train Loss: 0.1447 Acc: 0.9333 Pre: 0.9310 Recall: 0.9359 F1: 0.9335 Train AUC: 0.9845 Val AUC: 0.9794 Val PRC: 0.9784 Time: 0.72\n",
      "Epoch: 316 Train Loss: 0.1296 Acc: 0.9322 Pre: 0.9420 Recall: 0.9212 F1: 0.9315 Train AUC: 0.9880 Val AUC: 0.9778 Val PRC: 0.9798 Time: 0.71\n",
      "Epoch: 317 Train Loss: 0.1228 Acc: 0.9333 Pre: 0.9310 Recall: 0.9359 F1: 0.9335 Train AUC: 0.9894 Val AUC: 0.9792 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 318 Train Loss: 0.1465 Acc: 0.9312 Pre: 0.9381 Recall: 0.9233 F1: 0.9307 Train AUC: 0.9869 Val AUC: 0.9776 Val PRC: 0.9798 Time: 0.73\n",
      "Epoch: 319 Train Loss: 0.1413 Acc: 0.9312 Pre: 0.9372 Recall: 0.9244 F1: 0.9307 Train AUC: 0.9867 Val AUC: 0.9802 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 320 Train Loss: 0.1355 Acc: 0.9349 Pre: 0.9481 Recall: 0.9202 F1: 0.9339 Train AUC: 0.9873 Val AUC: 0.9785 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 321 Train Loss: 0.1359 Acc: 0.9301 Pre: 0.9333 Recall: 0.9265 F1: 0.9299 Train AUC: 0.9875 Val AUC: 0.9794 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 322 Train Loss: 0.1326 Acc: 0.9333 Pre: 0.9328 Recall: 0.9338 F1: 0.9333 Train AUC: 0.9871 Val AUC: 0.9811 Val PRC: 0.9828 Time: 0.70\n",
      "Epoch: 323 Train Loss: 0.1360 Acc: 0.9338 Pre: 0.9293 Recall: 0.9391 F1: 0.9342 Train AUC: 0.9867 Val AUC: 0.9791 Val PRC: 0.9792 Time: 0.70\n",
      "Epoch: 324 Train Loss: 0.1235 Acc: 0.9291 Pre: 0.9224 Recall: 0.9370 F1: 0.9297 Train AUC: 0.9893 Val AUC: 0.9792 Val PRC: 0.9800 Time: 0.73\n",
      "Epoch: 325 Train Loss: 0.1214 Acc: 0.9343 Pre: 0.9404 Recall: 0.9275 F1: 0.9339 Train AUC: 0.9896 Val AUC: 0.9791 Val PRC: 0.9788 Time: 0.71\n",
      "Epoch: 326 Train Loss: 0.1258 Acc: 0.9312 Pre: 0.9317 Recall: 0.9307 F1: 0.9312 Train AUC: 0.9887 Val AUC: 0.9803 Val PRC: 0.9814 Time: 0.70\n",
      "Epoch: 327 Train Loss: 0.1336 Acc: 0.9338 Pre: 0.9384 Recall: 0.9286 F1: 0.9335 Train AUC: 0.9876 Val AUC: 0.9789 Val PRC: 0.9782 Time: 0.72\n",
      "Epoch: 328 Train Loss: 0.1203 Acc: 0.9359 Pre: 0.9521 Recall: 0.9181 F1: 0.9348 Train AUC: 0.9894 Val AUC: 0.9802 Val PRC: 0.9814 Time: 0.70\n",
      "Epoch: 329 Train Loss: 0.1174 Acc: 0.9359 Pre: 0.9424 Recall: 0.9286 F1: 0.9354 Train AUC: 0.9899 Val AUC: 0.9789 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 330 Train Loss: 0.1245 Acc: 0.9370 Pre: 0.9464 Recall: 0.9265 F1: 0.9363 Train AUC: 0.9882 Val AUC: 0.9809 Val PRC: 0.9823 Time: 0.71\n",
      "Epoch: 331 Train Loss: 0.1255 Acc: 0.9343 Pre: 0.9432 Recall: 0.9244 F1: 0.9337 Train AUC: 0.9883 Val AUC: 0.9790 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 332 Train Loss: 0.1317 Acc: 0.9307 Pre: 0.9307 Recall: 0.9307 F1: 0.9307 Train AUC: 0.9869 Val AUC: 0.9798 Val PRC: 0.9806 Time: 0.70\n",
      "Epoch: 333 Train Loss: 0.1275 Acc: 0.9328 Pre: 0.9392 Recall: 0.9254 F1: 0.9323 Train AUC: 0.9875 Val AUC: 0.9794 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 334 Train Loss: 0.1206 Acc: 0.9254 Pre: 0.9141 Recall: 0.9391 F1: 0.9264 Train AUC: 0.9893 Val AUC: 0.9802 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 335 Train Loss: 0.1226 Acc: 0.9307 Pre: 0.9184 Recall: 0.9454 F1: 0.9317 Train AUC: 0.9895 Val AUC: 0.9791 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 336 Train Loss: 0.1272 Acc: 0.9359 Pre: 0.9287 Recall: 0.9443 F1: 0.9365 Train AUC: 0.9883 Val AUC: 0.9825 Val PRC: 0.9839 Time: 0.72\n",
      "Epoch: 337 Train Loss: 0.1372 Acc: 0.9270 Pre: 0.9248 Recall: 0.9296 F1: 0.9272 Train AUC: 0.9859 Val AUC: 0.9801 Val PRC: 0.9812 Time: 0.70\n",
      "Epoch: 338 Train Loss: 0.1320 Acc: 0.9307 Pre: 0.9437 Recall: 0.9160 F1: 0.9296 Train AUC: 0.9884 Val AUC: 0.9759 Val PRC: 0.9778 Time: 0.72\n",
      "Epoch: 339 Train Loss: 0.1308 Acc: 0.9307 Pre: 0.9390 Recall: 0.9212 F1: 0.9300 Train AUC: 0.9878 Val AUC: 0.9796 Val PRC: 0.9810 Time: 0.72\n",
      "Epoch: 340 Train Loss: 0.1230 Acc: 0.9286 Pre: 0.9130 Recall: 0.9475 F1: 0.9299 Train AUC: 0.9889 Val AUC: 0.9795 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 341 Train Loss: 0.1299 Acc: 0.9312 Pre: 0.9362 Recall: 0.9254 F1: 0.9308 Train AUC: 0.9885 Val AUC: 0.9790 Val PRC: 0.9807 Time: 0.73\n",
      "Epoch: 342 Train Loss: 0.1312 Acc: 0.9275 Pre: 0.9070 Recall: 0.9527 F1: 0.9293 Train AUC: 0.9877 Val AUC: 0.9815 Val PRC: 0.9830 Time: 0.70\n",
      "Epoch: 343 Train Loss: 0.1222 Acc: 0.9286 Pre: 0.9378 Recall: 0.9181 F1: 0.9278 Train AUC: 0.9897 Val AUC: 0.9789 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 344 Train Loss: 0.1075 Acc: 0.9343 Pre: 0.9470 Recall: 0.9202 F1: 0.9334 Train AUC: 0.9919 Val AUC: 0.9807 Val PRC: 0.9826 Time: 0.70\n",
      "Epoch: 345 Train Loss: 0.1238 Acc: 0.9317 Pre: 0.9290 Recall: 0.9349 F1: 0.9319 Train AUC: 0.9888 Val AUC: 0.9804 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 346 Train Loss: 0.1226 Acc: 0.9343 Pre: 0.9394 Recall: 0.9286 F1: 0.9340 Train AUC: 0.9876 Val AUC: 0.9804 Val PRC: 0.9824 Time: 0.73\n",
      "Epoch: 347 Train Loss: 0.1284 Acc: 0.9380 Pre: 0.9408 Recall: 0.9349 F1: 0.9378 Train AUC: 0.9872 Val AUC: 0.9798 Val PRC: 0.9806 Time: 0.74\n",
      "Epoch: 348 Train Loss: 0.1219 Acc: 0.9364 Pre: 0.9511 Recall: 0.9202 F1: 0.9354 Train AUC: 0.9891 Val AUC: 0.9802 Val PRC: 0.9816 Time: 0.72\n",
      "Epoch: 349 Train Loss: 0.1115 Acc: 0.9322 Pre: 0.9392 Recall: 0.9244 F1: 0.9317 Train AUC: 0.9911 Val AUC: 0.9815 Val PRC: 0.9825 Time: 0.74\n",
      "Epoch: 350 Train Loss: 0.1290 Acc: 0.9317 Pre: 0.9246 Recall: 0.9401 F1: 0.9323 Train AUC: 0.9894 Val AUC: 0.9799 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 351 Train Loss: 0.1240 Acc: 0.9391 Pre: 0.9437 Recall: 0.9338 F1: 0.9388 Train AUC: 0.9883 Val AUC: 0.9814 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 352 Train Loss: 0.1261 Acc: 0.9328 Pre: 0.9364 Recall: 0.9286 F1: 0.9325 Train AUC: 0.9877 Val AUC: 0.9808 Val PRC: 0.9829 Time: 0.73\n",
      "Epoch: 353 Train Loss: 0.1078 Acc: 0.9286 Pre: 0.9206 Recall: 0.9380 F1: 0.9292 Train AUC: 0.9914 Val AUC: 0.9797 Val PRC: 0.9812 Time: 0.72\n",
      "Epoch: 354 Train Loss: 0.1122 Acc: 0.9359 Pre: 0.9296 Recall: 0.9433 F1: 0.9364 Train AUC: 0.9908 Val AUC: 0.9796 Val PRC: 0.9819 Time: 0.72\n",
      "Epoch: 355 Train Loss: 0.1143 Acc: 0.9333 Pre: 0.9393 Recall: 0.9265 F1: 0.9328 Train AUC: 0.9903 Val AUC: 0.9797 Val PRC: 0.9812 Time: 0.75\n",
      "Epoch: 356 Train Loss: 0.1086 Acc: 0.9317 Pre: 0.9281 Recall: 0.9359 F1: 0.9320 Train AUC: 0.9910 Val AUC: 0.9818 Val PRC: 0.9830 Time: 0.73\n",
      "Epoch: 357 Train Loss: 0.1121 Acc: 0.9338 Pre: 0.9394 Recall: 0.9275 F1: 0.9334 Train AUC: 0.9904 Val AUC: 0.9792 Val PRC: 0.9814 Time: 0.73\n",
      "Epoch: 358 Train Loss: 0.1215 Acc: 0.9338 Pre: 0.9384 Recall: 0.9286 F1: 0.9335 Train AUC: 0.9889 Val AUC: 0.9813 Val PRC: 0.9832 Time: 0.73\n",
      "Epoch: 359 Train Loss: 0.1223 Acc: 0.9307 Pre: 0.9244 Recall: 0.9380 F1: 0.9312 Train AUC: 0.9881 Val AUC: 0.9816 Val PRC: 0.9828 Time: 0.72\n",
      "Epoch: 360 Train Loss: 0.1196 Acc: 0.9296 Pre: 0.9398 Recall: 0.9181 F1: 0.9288 Train AUC: 0.9892 Val AUC: 0.9789 Val PRC: 0.9805 Time: 0.72\n",
      "Epoch: 361 Train Loss: 0.1184 Acc: 0.9328 Pre: 0.9383 Recall: 0.9265 F1: 0.9323 Train AUC: 0.9886 Val AUC: 0.9805 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 362 Train Loss: 0.1171 Acc: 0.9312 Pre: 0.9236 Recall: 0.9401 F1: 0.9318 Train AUC: 0.9897 Val AUC: 0.9793 Val PRC: 0.9804 Time: 0.72\n",
      "Epoch: 363 Train Loss: 0.1043 Acc: 0.9349 Pre: 0.9331 Recall: 0.9370 F1: 0.9350 Train AUC: 0.9917 Val AUC: 0.9819 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 364 Train Loss: 0.1071 Acc: 0.9307 Pre: 0.9298 Recall: 0.9317 F1: 0.9307 Train AUC: 0.9915 Val AUC: 0.9797 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 365 Train Loss: 0.1179 Acc: 0.9338 Pre: 0.9206 Recall: 0.9496 F1: 0.9348 Train AUC: 0.9891 Val AUC: 0.9791 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 366 Train Loss: 0.1320 Acc: 0.9322 Pre: 0.9458 Recall: 0.9170 F1: 0.9312 Train AUC: 0.9887 Val AUC: 0.9779 Val PRC: 0.9795 Time: 0.70\n",
      "Epoch: 367 Train Loss: 0.1270 Acc: 0.9322 Pre: 0.9327 Recall: 0.9317 F1: 0.9322 Train AUC: 0.9867 Val AUC: 0.9796 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 368 Train Loss: 0.1345 Acc: 0.9286 Pre: 0.9277 Recall: 0.9296 F1: 0.9286 Train AUC: 0.9889 Val AUC: 0.9775 Val PRC: 0.9791 Time: 0.70\n",
      "Epoch: 369 Train Loss: 0.1142 Acc: 0.9286 Pre: 0.9259 Recall: 0.9317 F1: 0.9288 Train AUC: 0.9899 Val AUC: 0.9787 Val PRC: 0.9796 Time: 0.72\n",
      "Epoch: 370 Train Loss: 0.1044 Acc: 0.9322 Pre: 0.9255 Recall: 0.9401 F1: 0.9328 Train AUC: 0.9919 Val AUC: 0.9797 Val PRC: 0.9821 Time: 0.75\n",
      "Epoch: 371 Train Loss: 0.1126 Acc: 0.9343 Pre: 0.9276 Recall: 0.9422 F1: 0.9349 Train AUC: 0.9904 Val AUC: 0.9807 Val PRC: 0.9825 Time: 0.70\n",
      "Epoch: 372 Train Loss: 0.1137 Acc: 0.9333 Pre: 0.9292 Recall: 0.9380 F1: 0.9336 Train AUC: 0.9908 Val AUC: 0.9804 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 373 Train Loss: 0.1132 Acc: 0.9359 Pre: 0.9305 Recall: 0.9422 F1: 0.9363 Train AUC: 0.9909 Val AUC: 0.9805 Val PRC: 0.9803 Time: 0.71\n",
      "Epoch: 374 Train Loss: 0.1163 Acc: 0.9349 Pre: 0.9600 Recall: 0.9076 F1: 0.9330 Train AUC: 0.9892 Val AUC: 0.9797 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 375 Train Loss: 0.1122 Acc: 0.9349 Pre: 0.9452 Recall: 0.9233 F1: 0.9341 Train AUC: 0.9903 Val AUC: 0.9799 Val PRC: 0.9803 Time: 0.70\n",
      "Epoch: 376 Train Loss: 0.1160 Acc: 0.9291 Pre: 0.9305 Recall: 0.9275 F1: 0.9290 Train AUC: 0.9890 Val AUC: 0.9785 Val PRC: 0.9793 Time: 0.74\n",
      "Epoch: 377 Train Loss: 0.1157 Acc: 0.9317 Pre: 0.9477 Recall: 0.9139 F1: 0.9305 Train AUC: 0.9898 Val AUC: 0.9799 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 378 Train Loss: 0.1125 Acc: 0.9354 Pre: 0.9452 Recall: 0.9244 F1: 0.9347 Train AUC: 0.9901 Val AUC: 0.9800 Val PRC: 0.9829 Time: 0.69\n",
      "Epoch: 379 Train Loss: 0.1174 Acc: 0.9354 Pre: 0.9368 Recall: 0.9338 F1: 0.9353 Train AUC: 0.9895 Val AUC: 0.9798 Val PRC: 0.9813 Time: 0.69\n",
      "Epoch: 380 Train Loss: 0.1069 Acc: 0.9349 Pre: 0.9414 Recall: 0.9275 F1: 0.9344 Train AUC: 0.9914 Val AUC: 0.9807 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 381 Train Loss: 0.1161 Acc: 0.9349 Pre: 0.9277 Recall: 0.9433 F1: 0.9354 Train AUC: 0.9895 Val AUC: 0.9821 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 382 Train Loss: 0.1088 Acc: 0.9291 Pre: 0.9164 Recall: 0.9443 F1: 0.9302 Train AUC: 0.9918 Val AUC: 0.9807 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 383 Train Loss: 0.1161 Acc: 0.9364 Pre: 0.9244 Recall: 0.9506 F1: 0.9373 Train AUC: 0.9899 Val AUC: 0.9818 Val PRC: 0.9832 Time: 0.73\n",
      "Epoch: 384 Train Loss: 0.1077 Acc: 0.9317 Pre: 0.9317 Recall: 0.9317 F1: 0.9317 Train AUC: 0.9914 Val AUC: 0.9801 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 385 Train Loss: 0.1098 Acc: 0.9338 Pre: 0.9347 Recall: 0.9328 F1: 0.9338 Train AUC: 0.9909 Val AUC: 0.9800 Val PRC: 0.9811 Time: 0.72\n",
      "Epoch: 386 Train Loss: 0.1091 Acc: 0.9317 Pre: 0.9326 Recall: 0.9307 F1: 0.9317 Train AUC: 0.9913 Val AUC: 0.9795 Val PRC: 0.9815 Time: 0.71\n",
      "Epoch: 387 Train Loss: 0.1141 Acc: 0.9364 Pre: 0.9270 Recall: 0.9475 F1: 0.9371 Train AUC: 0.9902 Val AUC: 0.9807 Val PRC: 0.9827 Time: 0.70\n",
      "Epoch: 388 Train Loss: 0.1144 Acc: 0.9354 Pre: 0.9491 Recall: 0.9202 F1: 0.9344 Train AUC: 0.9904 Val AUC: 0.9789 Val PRC: 0.9761 Time: 0.70\n",
      "Epoch: 389 Train Loss: 0.1231 Acc: 0.9301 Pre: 0.9333 Recall: 0.9265 F1: 0.9299 Train AUC: 0.9877 Val AUC: 0.9772 Val PRC: 0.9756 Time: 0.72\n",
      "Epoch: 390 Train Loss: 0.1107 Acc: 0.9301 Pre: 0.9166 Recall: 0.9464 F1: 0.9313 Train AUC: 0.9903 Val AUC: 0.9812 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 391 Train Loss: 0.1102 Acc: 0.9354 Pre: 0.9405 Recall: 0.9296 F1: 0.9350 Train AUC: 0.9909 Val AUC: 0.9804 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 392 Train Loss: 0.1176 Acc: 0.9333 Pre: 0.9248 Recall: 0.9433 F1: 0.9340 Train AUC: 0.9893 Val AUC: 0.9800 Val PRC: 0.9798 Time: 0.72\n",
      "Epoch: 393 Train Loss: 0.1170 Acc: 0.9291 Pre: 0.9106 Recall: 0.9517 F1: 0.9307 Train AUC: 0.9898 Val AUC: 0.9810 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 394 Train Loss: 0.1152 Acc: 0.9291 Pre: 0.9323 Recall: 0.9254 F1: 0.9288 Train AUC: 0.9898 Val AUC: 0.9809 Val PRC: 0.9822 Time: 0.70\n",
      "Epoch: 395 Train Loss: 0.1231 Acc: 0.9307 Pre: 0.9244 Recall: 0.9380 F1: 0.9312 Train AUC: 0.9870 Val AUC: 0.9780 Val PRC: 0.9792 Time: 0.72\n",
      "Epoch: 396 Train Loss: 0.1198 Acc: 0.9338 Pre: 0.9422 Recall: 0.9244 F1: 0.9332 Train AUC: 0.9886 Val AUC: 0.9811 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 397 Train Loss: 0.1220 Acc: 0.9359 Pre: 0.9521 Recall: 0.9181 F1: 0.9348 Train AUC: 0.9882 Val AUC: 0.9793 Val PRC: 0.9788 Time: 0.91\n",
      "Epoch: 398 Train Loss: 0.1169 Acc: 0.9328 Pre: 0.9449 Recall: 0.9191 F1: 0.9318 Train AUC: 0.9900 Val AUC: 0.9797 Val PRC: 0.9803 Time: 0.73\n",
      "Epoch: 399 Train Loss: 0.1041 Acc: 0.9343 Pre: 0.9330 Recall: 0.9359 F1: 0.9345 Train AUC: 0.9915 Val AUC: 0.9793 Val PRC: 0.9793 Time: 0.70\n",
      "Epoch: 400 Train Loss: 0.1116 Acc: 0.9333 Pre: 0.9469 Recall: 0.9181 F1: 0.9323 Train AUC: 0.9902 Val AUC: 0.9787 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 401 Train Loss: 0.1065 Acc: 0.9328 Pre: 0.9301 Recall: 0.9359 F1: 0.9330 Train AUC: 0.9907 Val AUC: 0.9780 Val PRC: 0.9795 Time: 0.72\n",
      "Epoch: 402 Train Loss: 0.1054 Acc: 0.9349 Pre: 0.9481 Recall: 0.9202 F1: 0.9339 Train AUC: 0.9915 Val AUC: 0.9797 Val PRC: 0.9813 Time: 0.70\n",
      "Epoch: 403 Train Loss: 0.1135 Acc: 0.9322 Pre: 0.9449 Recall: 0.9181 F1: 0.9313 Train AUC: 0.9902 Val AUC: 0.9785 Val PRC: 0.9794 Time: 0.72\n",
      "Epoch: 404 Train Loss: 0.0993 Acc: 0.9338 Pre: 0.9284 Recall: 0.9401 F1: 0.9342 Train AUC: 0.9923 Val AUC: 0.9789 Val PRC: 0.9766 Time: 0.73\n",
      "Epoch: 405 Train Loss: 0.1052 Acc: 0.9354 Pre: 0.9443 Recall: 0.9254 F1: 0.9347 Train AUC: 0.9914 Val AUC: 0.9797 Val PRC: 0.9814 Time: 0.74\n",
      "Epoch: 406 Train Loss: 0.1086 Acc: 0.9354 Pre: 0.9471 Recall: 0.9223 F1: 0.9345 Train AUC: 0.9909 Val AUC: 0.9796 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 407 Train Loss: 0.1071 Acc: 0.9343 Pre: 0.9480 Recall: 0.9191 F1: 0.9333 Train AUC: 0.9910 Val AUC: 0.9786 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 408 Train Loss: 0.1025 Acc: 0.9380 Pre: 0.9380 Recall: 0.9380 F1: 0.9380 Train AUC: 0.9916 Val AUC: 0.9800 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 409 Train Loss: 0.0982 Acc: 0.9338 Pre: 0.9412 Recall: 0.9254 F1: 0.9333 Train AUC: 0.9922 Val AUC: 0.9820 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 410 Train Loss: 0.1033 Acc: 0.9354 Pre: 0.9396 Recall: 0.9307 F1: 0.9351 Train AUC: 0.9917 Val AUC: 0.9804 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 411 Train Loss: 0.1146 Acc: 0.9370 Pre: 0.9454 Recall: 0.9275 F1: 0.9364 Train AUC: 0.9892 Val AUC: 0.9811 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 412 Train Loss: 0.1114 Acc: 0.9333 Pre: 0.9374 Recall: 0.9286 F1: 0.9330 Train AUC: 0.9901 Val AUC: 0.9801 Val PRC: 0.9823 Time: 0.70\n",
      "Epoch: 413 Train Loss: 0.1034 Acc: 0.9370 Pre: 0.9370 Recall: 0.9370 F1: 0.9370 Train AUC: 0.9914 Val AUC: 0.9807 Val PRC: 0.9817 Time: 0.71\n",
      "Epoch: 414 Train Loss: 0.1022 Acc: 0.9328 Pre: 0.9478 Recall: 0.9160 F1: 0.9316 Train AUC: 0.9917 Val AUC: 0.9799 Val PRC: 0.9787 Time: 0.71\n",
      "Epoch: 415 Train Loss: 0.1052 Acc: 0.9338 Pre: 0.9412 Recall: 0.9254 F1: 0.9333 Train AUC: 0.9909 Val AUC: 0.9790 Val PRC: 0.9809 Time: 0.70\n",
      "Epoch: 416 Train Loss: 0.1114 Acc: 0.9401 Pre: 0.9584 Recall: 0.9202 F1: 0.9389 Train AUC: 0.9901 Val AUC: 0.9800 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 417 Train Loss: 0.1060 Acc: 0.9386 Pre: 0.9465 Recall: 0.9296 F1: 0.9380 Train AUC: 0.9907 Val AUC: 0.9810 Val PRC: 0.9827 Time: 0.71\n",
      "Epoch: 418 Train Loss: 0.1117 Acc: 0.9375 Pre: 0.9361 Recall: 0.9391 F1: 0.9376 Train AUC: 0.9899 Val AUC: 0.9806 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 419 Train Loss: 0.1107 Acc: 0.9380 Pre: 0.9408 Recall: 0.9349 F1: 0.9378 Train AUC: 0.9894 Val AUC: 0.9807 Val PRC: 0.9816 Time: 0.71\n",
      "Epoch: 420 Train Loss: 0.1099 Acc: 0.9359 Pre: 0.9396 Recall: 0.9317 F1: 0.9357 Train AUC: 0.9902 Val AUC: 0.9815 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 421 Train Loss: 0.0999 Acc: 0.9317 Pre: 0.9185 Recall: 0.9475 F1: 0.9328 Train AUC: 0.9916 Val AUC: 0.9805 Val PRC: 0.9823 Time: 0.71\n",
      "Epoch: 422 Train Loss: 0.1058 Acc: 0.9354 Pre: 0.9452 Recall: 0.9244 F1: 0.9347 Train AUC: 0.9907 Val AUC: 0.9805 Val PRC: 0.9819 Time: 0.70\n",
      "Epoch: 423 Train Loss: 0.1074 Acc: 0.9328 Pre: 0.9256 Recall: 0.9412 F1: 0.9333 Train AUC: 0.9916 Val AUC: 0.9813 Val PRC: 0.9831 Time: 0.71\n",
      "Epoch: 424 Train Loss: 0.1059 Acc: 0.9386 Pre: 0.9309 Recall: 0.9475 F1: 0.9391 Train AUC: 0.9905 Val AUC: 0.9813 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 425 Train Loss: 0.1161 Acc: 0.9338 Pre: 0.9479 Recall: 0.9181 F1: 0.9328 Train AUC: 0.9903 Val AUC: 0.9801 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 426 Train Loss: 0.0927 Acc: 0.9359 Pre: 0.9323 Recall: 0.9401 F1: 0.9362 Train AUC: 0.9931 Val AUC: 0.9824 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 427 Train Loss: 0.1122 Acc: 0.9359 Pre: 0.9501 Recall: 0.9202 F1: 0.9349 Train AUC: 0.9904 Val AUC: 0.9811 Val PRC: 0.9822 Time: 0.70\n",
      "Epoch: 428 Train Loss: 0.1024 Acc: 0.9354 Pre: 0.9462 Recall: 0.9233 F1: 0.9346 Train AUC: 0.9907 Val AUC: 0.9816 Val PRC: 0.9831 Time: 0.70\n",
      "Epoch: 429 Train Loss: 0.1122 Acc: 0.9286 Pre: 0.9378 Recall: 0.9181 F1: 0.9278 Train AUC: 0.9907 Val AUC: 0.9799 Val PRC: 0.9812 Time: 0.74\n",
      "Epoch: 430 Train Loss: 0.1069 Acc: 0.9343 Pre: 0.9499 Recall: 0.9170 F1: 0.9332 Train AUC: 0.9908 Val AUC: 0.9802 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 431 Train Loss: 0.0968 Acc: 0.9375 Pre: 0.9474 Recall: 0.9265 F1: 0.9368 Train AUC: 0.9920 Val AUC: 0.9816 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 432 Train Loss: 0.1033 Acc: 0.9333 Pre: 0.9558 Recall: 0.9086 F1: 0.9316 Train AUC: 0.9921 Val AUC: 0.9794 Val PRC: 0.9799 Time: 0.74\n",
      "Epoch: 433 Train Loss: 0.1025 Acc: 0.9375 Pre: 0.9474 Recall: 0.9265 F1: 0.9368 Train AUC: 0.9908 Val AUC: 0.9816 Val PRC: 0.9834 Time: 0.72\n",
      "Epoch: 434 Train Loss: 0.1012 Acc: 0.9391 Pre: 0.9504 Recall: 0.9265 F1: 0.9383 Train AUC: 0.9914 Val AUC: 0.9821 Val PRC: 0.9836 Time: 0.70\n",
      "Epoch: 435 Train Loss: 0.1240 Acc: 0.9312 Pre: 0.9447 Recall: 0.9160 F1: 0.9301 Train AUC: 0.9907 Val AUC: 0.9803 Val PRC: 0.9816 Time: 0.75\n",
      "Epoch: 436 Train Loss: 0.1018 Acc: 0.9359 Pre: 0.9462 Recall: 0.9244 F1: 0.9352 Train AUC: 0.9920 Val AUC: 0.9817 Val PRC: 0.9838 Time: 0.73\n",
      "Epoch: 437 Train Loss: 0.1128 Acc: 0.9386 Pre: 0.9623 Recall: 0.9128 F1: 0.9369 Train AUC: 0.9903 Val AUC: 0.9829 Val PRC: 0.9845 Time: 0.70\n",
      "Epoch: 438 Train Loss: 0.0990 Acc: 0.9359 Pre: 0.9270 Recall: 0.9464 F1: 0.9366 Train AUC: 0.9917 Val AUC: 0.9823 Val PRC: 0.9838 Time: 0.72\n",
      "Epoch: 439 Train Loss: 0.1030 Acc: 0.9364 Pre: 0.9511 Recall: 0.9202 F1: 0.9354 Train AUC: 0.9916 Val AUC: 0.9818 Val PRC: 0.9797 Time: 0.73\n",
      "Epoch: 440 Train Loss: 0.1007 Acc: 0.9312 Pre: 0.9263 Recall: 0.9370 F1: 0.9316 Train AUC: 0.9917 Val AUC: 0.9836 Val PRC: 0.9850 Time: 0.70\n",
      "Epoch: 441 Train Loss: 0.0991 Acc: 0.9349 Pre: 0.9471 Recall: 0.9212 F1: 0.9340 Train AUC: 0.9914 Val AUC: 0.9812 Val PRC: 0.9824 Time: 0.72\n",
      "Epoch: 442 Train Loss: 0.0994 Acc: 0.9391 Pre: 0.9504 Recall: 0.9265 F1: 0.9383 Train AUC: 0.9916 Val AUC: 0.9824 Val PRC: 0.9826 Time: 0.70\n",
      "Epoch: 443 Train Loss: 0.1093 Acc: 0.9359 Pre: 0.9396 Recall: 0.9317 F1: 0.9357 Train AUC: 0.9902 Val AUC: 0.9804 Val PRC: 0.9824 Time: 0.70\n",
      "Epoch: 444 Train Loss: 0.1023 Acc: 0.9359 Pre: 0.9368 Recall: 0.9349 F1: 0.9359 Train AUC: 0.9916 Val AUC: 0.9808 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 445 Train Loss: 0.1053 Acc: 0.9386 Pre: 0.9456 Recall: 0.9307 F1: 0.9381 Train AUC: 0.9904 Val AUC: 0.9805 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 446 Train Loss: 0.0954 Acc: 0.9354 Pre: 0.9443 Recall: 0.9254 F1: 0.9347 Train AUC: 0.9921 Val AUC: 0.9814 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 447 Train Loss: 0.0962 Acc: 0.9370 Pre: 0.9541 Recall: 0.9181 F1: 0.9358 Train AUC: 0.9924 Val AUC: 0.9802 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 448 Train Loss: 0.0961 Acc: 0.9391 Pre: 0.9456 Recall: 0.9317 F1: 0.9386 Train AUC: 0.9923 Val AUC: 0.9807 Val PRC: 0.9830 Time: 0.70\n",
      "Epoch: 449 Train Loss: 0.0959 Acc: 0.9349 Pre: 0.9580 Recall: 0.9097 F1: 0.9332 Train AUC: 0.9921 Val AUC: 0.9794 Val PRC: 0.9813 Time: 0.71\n",
      "Epoch: 450 Train Loss: 0.1042 Acc: 0.9380 Pre: 0.9513 Recall: 0.9233 F1: 0.9371 Train AUC: 0.9905 Val AUC: 0.9813 Val PRC: 0.9831 Time: 0.70\n",
      "Epoch: 451 Train Loss: 0.1024 Acc: 0.9359 Pre: 0.9368 Recall: 0.9349 F1: 0.9359 Train AUC: 0.9929 Val AUC: 0.9816 Val PRC: 0.9832 Time: 0.69\n",
      "Epoch: 452 Train Loss: 0.0964 Acc: 0.9364 Pre: 0.9351 Recall: 0.9380 F1: 0.9365 Train AUC: 0.9921 Val AUC: 0.9803 Val PRC: 0.9823 Time: 0.70\n",
      "Epoch: 453 Train Loss: 0.1121 Acc: 0.9354 Pre: 0.9433 Recall: 0.9265 F1: 0.9348 Train AUC: 0.9911 Val AUC: 0.9808 Val PRC: 0.9824 Time: 0.72\n",
      "Epoch: 454 Train Loss: 0.0964 Acc: 0.9428 Pre: 0.9442 Recall: 0.9412 F1: 0.9427 Train AUC: 0.9919 Val AUC: 0.9816 Val PRC: 0.9831 Time: 0.70\n",
      "Epoch: 455 Train Loss: 0.1054 Acc: 0.9370 Pre: 0.9454 Recall: 0.9275 F1: 0.9364 Train AUC: 0.9898 Val AUC: 0.9818 Val PRC: 0.9827 Time: 0.70\n",
      "Epoch: 456 Train Loss: 0.0898 Acc: 0.9354 Pre: 0.9359 Recall: 0.9349 F1: 0.9354 Train AUC: 0.9930 Val AUC: 0.9829 Val PRC: 0.9848 Time: 0.70\n",
      "Epoch: 457 Train Loss: 0.0953 Acc: 0.9359 Pre: 0.9314 Recall: 0.9412 F1: 0.9363 Train AUC: 0.9925 Val AUC: 0.9828 Val PRC: 0.9846 Time: 0.73\n",
      "Epoch: 458 Train Loss: 0.1059 Acc: 0.9417 Pre: 0.9536 Recall: 0.9286 F1: 0.9409 Train AUC: 0.9902 Val AUC: 0.9838 Val PRC: 0.9853 Time: 0.74\n",
      "Epoch: 459 Train Loss: 0.0994 Acc: 0.9349 Pre: 0.9322 Recall: 0.9380 F1: 0.9351 Train AUC: 0.9917 Val AUC: 0.9817 Val PRC: 0.9830 Time: 0.75\n",
      "Epoch: 460 Train Loss: 0.0997 Acc: 0.9370 Pre: 0.9541 Recall: 0.9181 F1: 0.9358 Train AUC: 0.9911 Val AUC: 0.9817 Val PRC: 0.9832 Time: 0.73\n",
      "Epoch: 461 Train Loss: 0.0943 Acc: 0.9333 Pre: 0.9347 Recall: 0.9317 F1: 0.9332 Train AUC: 0.9921 Val AUC: 0.9819 Val PRC: 0.9843 Time: 0.70\n",
      "Epoch: 462 Train Loss: 0.0904 Acc: 0.9333 Pre: 0.9365 Recall: 0.9296 F1: 0.9331 Train AUC: 0.9928 Val AUC: 0.9833 Val PRC: 0.9853 Time: 0.71\n",
      "Epoch: 463 Train Loss: 0.0867 Acc: 0.9391 Pre: 0.9583 Recall: 0.9181 F1: 0.9378 Train AUC: 0.9938 Val AUC: 0.9818 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 464 Train Loss: 0.0995 Acc: 0.9380 Pre: 0.9523 Recall: 0.9223 F1: 0.9370 Train AUC: 0.9913 Val AUC: 0.9836 Val PRC: 0.9850 Time: 0.72\n",
      "Epoch: 465 Train Loss: 0.0906 Acc: 0.9380 Pre: 0.9446 Recall: 0.9307 F1: 0.9376 Train AUC: 0.9931 Val AUC: 0.9830 Val PRC: 0.9850 Time: 0.73\n",
      "Epoch: 466 Train Loss: 0.0919 Acc: 0.9412 Pre: 0.9375 Recall: 0.9454 F1: 0.9414 Train AUC: 0.9923 Val AUC: 0.9833 Val PRC: 0.9852 Time: 0.74\n",
      "Epoch: 467 Train Loss: 0.1113 Acc: 0.9364 Pre: 0.9388 Recall: 0.9338 F1: 0.9363 Train AUC: 0.9912 Val AUC: 0.9820 Val PRC: 0.9842 Time: 0.72\n",
      "Epoch: 468 Train Loss: 0.0929 Acc: 0.9417 Pre: 0.9478 Recall: 0.9349 F1: 0.9413 Train AUC: 0.9925 Val AUC: 0.9840 Val PRC: 0.9854 Time: 0.74\n",
      "Epoch: 469 Train Loss: 0.0848 Acc: 0.9454 Pre: 0.9599 Recall: 0.9296 F1: 0.9445 Train AUC: 0.9939 Val AUC: 0.9820 Val PRC: 0.9843 Time: 0.75\n",
      "Epoch: 470 Train Loss: 0.0987 Acc: 0.9401 Pre: 0.9656 Recall: 0.9128 F1: 0.9384 Train AUC: 0.9911 Val AUC: 0.9823 Val PRC: 0.9846 Time: 0.70\n",
      "Epoch: 471 Train Loss: 0.1005 Acc: 0.9401 Pre: 0.9515 Recall: 0.9275 F1: 0.9394 Train AUC: 0.9931 Val AUC: 0.9818 Val PRC: 0.9833 Time: 0.70\n",
      "Epoch: 472 Train Loss: 0.0833 Acc: 0.9386 Pre: 0.9504 Recall: 0.9254 F1: 0.9377 Train AUC: 0.9934 Val AUC: 0.9825 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 473 Train Loss: 0.0879 Acc: 0.9407 Pre: 0.9487 Recall: 0.9317 F1: 0.9401 Train AUC: 0.9929 Val AUC: 0.9828 Val PRC: 0.9838 Time: 0.71\n",
      "Epoch: 474 Train Loss: 0.0848 Acc: 0.9359 Pre: 0.9415 Recall: 0.9296 F1: 0.9355 Train AUC: 0.9935 Val AUC: 0.9813 Val PRC: 0.9835 Time: 0.73\n",
      "Epoch: 475 Train Loss: 0.0817 Acc: 0.9380 Pre: 0.9572 Recall: 0.9170 F1: 0.9367 Train AUC: 0.9946 Val AUC: 0.9840 Val PRC: 0.9855 Time: 0.73\n",
      "Epoch: 476 Train Loss: 0.0938 Acc: 0.9386 Pre: 0.9583 Recall: 0.9170 F1: 0.9372 Train AUC: 0.9912 Val AUC: 0.9838 Val PRC: 0.9851 Time: 0.72\n",
      "Epoch: 477 Train Loss: 0.0886 Acc: 0.9380 Pre: 0.9344 Recall: 0.9422 F1: 0.9383 Train AUC: 0.9928 Val AUC: 0.9825 Val PRC: 0.9818 Time: 0.71\n",
      "Epoch: 478 Train Loss: 0.1002 Acc: 0.9391 Pre: 0.9543 Recall: 0.9223 F1: 0.9380 Train AUC: 0.9911 Val AUC: 0.9829 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 479 Train Loss: 0.0828 Acc: 0.9354 Pre: 0.9414 Recall: 0.9286 F1: 0.9350 Train AUC: 0.9942 Val AUC: 0.9819 Val PRC: 0.9836 Time: 0.70\n",
      "Epoch: 480 Train Loss: 0.0873 Acc: 0.9349 Pre: 0.9312 Recall: 0.9391 F1: 0.9351 Train AUC: 0.9931 Val AUC: 0.9824 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 481 Train Loss: 0.0815 Acc: 0.9391 Pre: 0.9524 Recall: 0.9244 F1: 0.9382 Train AUC: 0.9943 Val AUC: 0.9823 Val PRC: 0.9826 Time: 0.75\n",
      "Epoch: 482 Train Loss: 0.0907 Acc: 0.9359 Pre: 0.9396 Recall: 0.9317 F1: 0.9357 Train AUC: 0.9926 Val AUC: 0.9830 Val PRC: 0.9844 Time: 0.71\n",
      "Epoch: 483 Train Loss: 0.0848 Acc: 0.9386 Pre: 0.9553 Recall: 0.9202 F1: 0.9374 Train AUC: 0.9939 Val AUC: 0.9809 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 484 Train Loss: 0.0970 Acc: 0.9370 Pre: 0.9561 Recall: 0.9160 F1: 0.9356 Train AUC: 0.9914 Val AUC: 0.9815 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 485 Train Loss: 0.0883 Acc: 0.9375 Pre: 0.9334 Recall: 0.9422 F1: 0.9378 Train AUC: 0.9931 Val AUC: 0.9797 Val PRC: 0.9801 Time: 0.71\n",
      "Epoch: 486 Train Loss: 0.0908 Acc: 0.9359 Pre: 0.9443 Recall: 0.9265 F1: 0.9353 Train AUC: 0.9923 Val AUC: 0.9800 Val PRC: 0.9803 Time: 0.72\n",
      "Epoch: 487 Train Loss: 0.0869 Acc: 0.9370 Pre: 0.9454 Recall: 0.9275 F1: 0.9364 Train AUC: 0.9930 Val AUC: 0.9811 Val PRC: 0.9825 Time: 0.73\n",
      "Epoch: 488 Train Loss: 0.0920 Acc: 0.9370 Pre: 0.9512 Recall: 0.9212 F1: 0.9360 Train AUC: 0.9919 Val AUC: 0.9817 Val PRC: 0.9832 Time: 0.73\n",
      "Epoch: 489 Train Loss: 0.0866 Acc: 0.9312 Pre: 0.9317 Recall: 0.9307 F1: 0.9312 Train AUC: 0.9932 Val AUC: 0.9811 Val PRC: 0.9834 Time: 0.73\n",
      "Epoch: 490 Train Loss: 0.0837 Acc: 0.9386 Pre: 0.9475 Recall: 0.9286 F1: 0.9379 Train AUC: 0.9936 Val AUC: 0.9837 Val PRC: 0.9853 Time: 0.72\n",
      "Epoch: 491 Train Loss: 0.0966 Acc: 0.9412 Pre: 0.9536 Recall: 0.9275 F1: 0.9404 Train AUC: 0.9930 Val AUC: 0.9823 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 492 Train Loss: 0.0923 Acc: 0.9364 Pre: 0.9492 Recall: 0.9223 F1: 0.9355 Train AUC: 0.9917 Val AUC: 0.9823 Val PRC: 0.9831 Time: 0.71\n",
      "Epoch: 493 Train Loss: 0.0860 Acc: 0.9349 Pre: 0.9442 Recall: 0.9244 F1: 0.9342 Train AUC: 0.9934 Val AUC: 0.9815 Val PRC: 0.9821 Time: 0.73\n",
      "Epoch: 494 Train Loss: 0.0931 Acc: 0.9343 Pre: 0.9294 Recall: 0.9401 F1: 0.9347 Train AUC: 0.9922 Val AUC: 0.9810 Val PRC: 0.9829 Time: 0.75\n",
      "Epoch: 495 Train Loss: 0.0860 Acc: 0.9364 Pre: 0.9388 Recall: 0.9338 F1: 0.9363 Train AUC: 0.9933 Val AUC: 0.9821 Val PRC: 0.9839 Time: 0.73\n",
      "Epoch: 496 Train Loss: 0.0917 Acc: 0.9343 Pre: 0.9330 Recall: 0.9359 F1: 0.9345 Train AUC: 0.9927 Val AUC: 0.9818 Val PRC: 0.9830 Time: 0.73\n",
      "Epoch: 497 Train Loss: 0.0899 Acc: 0.9312 Pre: 0.9219 Recall: 0.9422 F1: 0.9319 Train AUC: 0.9919 Val AUC: 0.9814 Val PRC: 0.9795 Time: 0.71\n",
      "Epoch: 498 Train Loss: 0.0884 Acc: 0.9354 Pre: 0.9377 Recall: 0.9328 F1: 0.9352 Train AUC: 0.9930 Val AUC: 0.9820 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 499 Train Loss: 0.0853 Acc: 0.9370 Pre: 0.9298 Recall: 0.9454 F1: 0.9375 Train AUC: 0.9936 Val AUC: 0.9833 Val PRC: 0.9850 Time: 0.73\n",
      "Epoch: 500 Train Loss: 0.0845 Acc: 0.9354 Pre: 0.9304 Recall: 0.9412 F1: 0.9358 Train AUC: 0.9932 Val AUC: 0.9821 Val PRC: 0.9843 Time: 0.72\n",
      "Fold: 1 Best Epoch: 458 Val acc: 0.9417 Val Pre: 0.9536 Val Recall: 0.9286 Val F1: 0.9409 Val AUC: 0.9838 Val PRC: 0.9853\n",
      "------this is 2th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.6966 Acc: 0.5236 Pre: 0.5124 Recall: 0.9790 F1: 0.6727 Train AUC: 0.4759 Val AUC: 0.4850 Val PRC: 0.4729 Time: 0.76\n",
      "Epoch: 2 Train Loss: 0.6910 Acc: 0.5431 Pre: 0.5228 Recall: 0.9884 F1: 0.6839 Train AUC: 0.5124 Val AUC: 0.5375 Val PRC: 0.5236 Time: 0.74\n",
      "Epoch: 3 Train Loss: 0.6860 Acc: 0.5546 Pre: 0.5308 Recall: 0.9412 F1: 0.6788 Train AUC: 0.5639 Val AUC: 0.5675 Val PRC: 0.5310 Time: 0.74\n",
      "Epoch: 4 Train Loss: 0.6805 Acc: 0.5310 Pre: 0.5162 Recall: 0.9853 F1: 0.6775 Train AUC: 0.5947 Val AUC: 0.5905 Val PRC: 0.5746 Time: 0.73\n",
      "Epoch: 5 Train Loss: 0.6724 Acc: 0.6008 Pre: 0.5635 Recall: 0.8950 F1: 0.6916 Train AUC: 0.6621 Val AUC: 0.6597 Val PRC: 0.6437 Time: 0.72\n",
      "Epoch: 6 Train Loss: 0.6699 Acc: 0.5720 Pre: 0.5417 Recall: 0.9349 F1: 0.6859 Train AUC: 0.6812 Val AUC: 0.6794 Val PRC: 0.6654 Time: 0.77\n",
      "Epoch: 7 Train Loss: 0.6742 Acc: 0.5720 Pre: 0.5424 Recall: 0.9202 F1: 0.6825 Train AUC: 0.6556 Val AUC: 0.6634 Val PRC: 0.6438 Time: 0.73\n",
      "Epoch: 8 Train Loss: 0.6652 Acc: 0.5882 Pre: 0.5524 Recall: 0.9307 F1: 0.6933 Train AUC: 0.6943 Val AUC: 0.6952 Val PRC: 0.6861 Time: 0.72\n",
      "Epoch: 9 Train Loss: 0.6629 Acc: 0.5993 Pre: 0.5581 Recall: 0.9538 F1: 0.7041 Train AUC: 0.7119 Val AUC: 0.7103 Val PRC: 0.6933 Time: 0.73\n",
      "Epoch: 10 Train Loss: 0.6645 Acc: 0.6523 Pre: 0.6112 Recall: 0.8372 F1: 0.7066 Train AUC: 0.7083 Val AUC: 0.7296 Val PRC: 0.7045 Time: 0.74\n",
      "Epoch: 11 Train Loss: 0.6575 Acc: 0.6633 Pre: 0.6188 Recall: 0.8508 F1: 0.7165 Train AUC: 0.7269 Val AUC: 0.7558 Val PRC: 0.7574 Time: 0.75\n",
      "Epoch: 12 Train Loss: 0.6432 Acc: 0.7006 Pre: 0.6711 Recall: 0.7868 F1: 0.7244 Train AUC: 0.7707 Val AUC: 0.7856 Val PRC: 0.8020 Time: 0.75\n",
      "Epoch: 13 Train Loss: 0.6441 Acc: 0.6686 Pre: 0.6228 Recall: 0.8550 F1: 0.7207 Train AUC: 0.7740 Val AUC: 0.7839 Val PRC: 0.7883 Time: 0.72\n",
      "Epoch: 14 Train Loss: 0.6383 Acc: 0.7059 Pre: 0.6756 Recall: 0.7920 F1: 0.7292 Train AUC: 0.7867 Val AUC: 0.7981 Val PRC: 0.8105 Time: 0.73\n",
      "Epoch: 15 Train Loss: 0.6298 Acc: 0.6933 Pre: 0.6611 Recall: 0.7931 F1: 0.7211 Train AUC: 0.7933 Val AUC: 0.7894 Val PRC: 0.8087 Time: 0.74\n",
      "Epoch: 16 Train Loss: 0.6253 Acc: 0.7222 Pre: 0.7055 Recall: 0.7626 F1: 0.7330 Train AUC: 0.8042 Val AUC: 0.8114 Val PRC: 0.8283 Time: 0.70\n",
      "Epoch: 17 Train Loss: 0.6135 Acc: 0.7064 Pre: 0.6617 Recall: 0.8445 F1: 0.7420 Train AUC: 0.8090 Val AUC: 0.8195 Val PRC: 0.8378 Time: 0.73\n",
      "Epoch: 18 Train Loss: 0.6129 Acc: 0.7458 Pre: 0.7543 Recall: 0.7290 F1: 0.7415 Train AUC: 0.8139 Val AUC: 0.8226 Val PRC: 0.8398 Time: 0.74\n",
      "Epoch: 19 Train Loss: 0.6091 Acc: 0.7096 Pre: 0.6615 Recall: 0.8582 F1: 0.7471 Train AUC: 0.8232 Val AUC: 0.8300 Val PRC: 0.8456 Time: 0.71\n",
      "Epoch: 20 Train Loss: 0.5892 Acc: 0.7700 Pre: 0.7927 Recall: 0.7311 F1: 0.7607 Train AUC: 0.8358 Val AUC: 0.8451 Val PRC: 0.8684 Time: 0.73\n",
      "Epoch: 21 Train Loss: 0.5838 Acc: 0.7442 Pre: 0.7360 Recall: 0.7616 F1: 0.7486 Train AUC: 0.8204 Val AUC: 0.8328 Val PRC: 0.8561 Time: 0.72\n",
      "Epoch: 22 Train Loss: 0.5828 Acc: 0.7463 Pre: 0.7292 Recall: 0.7836 F1: 0.7554 Train AUC: 0.8262 Val AUC: 0.8323 Val PRC: 0.8541 Time: 0.70\n",
      "Epoch: 23 Train Loss: 0.5763 Acc: 0.7600 Pre: 0.7670 Recall: 0.7468 F1: 0.7568 Train AUC: 0.8316 Val AUC: 0.8360 Val PRC: 0.8561 Time: 0.71\n",
      "Epoch: 24 Train Loss: 0.5613 Acc: 0.7468 Pre: 0.7350 Recall: 0.7721 F1: 0.7531 Train AUC: 0.8372 Val AUC: 0.8410 Val PRC: 0.8661 Time: 0.74\n",
      "Epoch: 25 Train Loss: 0.5515 Acc: 0.7411 Pre: 0.7171 Recall: 0.7962 F1: 0.7546 Train AUC: 0.8461 Val AUC: 0.8420 Val PRC: 0.8632 Time: 0.72\n",
      "Epoch: 26 Train Loss: 0.5504 Acc: 0.7742 Pre: 0.8137 Recall: 0.7111 F1: 0.7590 Train AUC: 0.8406 Val AUC: 0.8489 Val PRC: 0.8695 Time: 0.72\n",
      "Epoch: 27 Train Loss: 0.5339 Acc: 0.7673 Pre: 0.7788 Recall: 0.7468 F1: 0.7625 Train AUC: 0.8454 Val AUC: 0.8505 Val PRC: 0.8740 Time: 0.95\n",
      "Epoch: 28 Train Loss: 0.5227 Acc: 0.7642 Pre: 0.7874 Recall: 0.7237 F1: 0.7542 Train AUC: 0.8521 Val AUC: 0.8481 Val PRC: 0.8732 Time: 0.72\n",
      "Epoch: 29 Train Loss: 0.5160 Acc: 0.7705 Pre: 0.7864 Recall: 0.7426 F1: 0.7639 Train AUC: 0.8553 Val AUC: 0.8530 Val PRC: 0.8749 Time: 0.71\n",
      "Epoch: 30 Train Loss: 0.5107 Acc: 0.7637 Pre: 0.7670 Recall: 0.7574 F1: 0.7622 Train AUC: 0.8530 Val AUC: 0.8535 Val PRC: 0.8773 Time: 0.73\n",
      "Epoch: 31 Train Loss: 0.5039 Acc: 0.7574 Pre: 0.7431 Recall: 0.7868 F1: 0.7643 Train AUC: 0.8617 Val AUC: 0.8492 Val PRC: 0.8677 Time: 0.70\n",
      "Epoch: 32 Train Loss: 0.4814 Acc: 0.7941 Pre: 0.8302 Recall: 0.7395 F1: 0.7822 Train AUC: 0.8660 Val AUC: 0.8667 Val PRC: 0.8896 Time: 0.71\n",
      "Epoch: 33 Train Loss: 0.4858 Acc: 0.7752 Pre: 0.7984 Recall: 0.7363 F1: 0.7661 Train AUC: 0.8564 Val AUC: 0.8579 Val PRC: 0.8844 Time: 0.73\n",
      "Epoch: 34 Train Loss: 0.4820 Acc: 0.7752 Pre: 0.7842 Recall: 0.7595 F1: 0.7716 Train AUC: 0.8576 Val AUC: 0.8605 Val PRC: 0.8821 Time: 0.71\n",
      "Epoch: 35 Train Loss: 0.4723 Acc: 0.7841 Pre: 0.8149 Recall: 0.7353 F1: 0.7731 Train AUC: 0.8618 Val AUC: 0.8606 Val PRC: 0.8856 Time: 0.74\n",
      "Epoch: 36 Train Loss: 0.4597 Acc: 0.7925 Pre: 0.8250 Recall: 0.7426 F1: 0.7816 Train AUC: 0.8680 Val AUC: 0.8685 Val PRC: 0.8939 Time: 0.73\n",
      "Epoch: 37 Train Loss: 0.4576 Acc: 0.7952 Pre: 0.8504 Recall: 0.7164 F1: 0.7777 Train AUC: 0.8726 Val AUC: 0.8655 Val PRC: 0.8878 Time: 0.73\n",
      "Epoch: 38 Train Loss: 0.4470 Acc: 0.7988 Pre: 0.8457 Recall: 0.7311 F1: 0.7842 Train AUC: 0.8742 Val AUC: 0.8673 Val PRC: 0.8941 Time: 0.71\n",
      "Epoch: 39 Train Loss: 0.4384 Acc: 0.8083 Pre: 0.8710 Recall: 0.7237 F1: 0.7906 Train AUC: 0.8776 Val AUC: 0.8723 Val PRC: 0.8965 Time: 0.75\n",
      "Epoch: 40 Train Loss: 0.4302 Acc: 0.8062 Pre: 0.8685 Recall: 0.7216 F1: 0.7883 Train AUC: 0.8790 Val AUC: 0.8722 Val PRC: 0.8985 Time: 0.73\n",
      "Epoch: 41 Train Loss: 0.4314 Acc: 0.8141 Pre: 0.9130 Recall: 0.6943 F1: 0.7888 Train AUC: 0.8748 Val AUC: 0.8731 Val PRC: 0.8985 Time: 0.77\n",
      "Epoch: 42 Train Loss: 0.4290 Acc: 0.8141 Pre: 0.8955 Recall: 0.7111 F1: 0.7927 Train AUC: 0.8802 Val AUC: 0.8724 Val PRC: 0.8956 Time: 0.76\n",
      "Epoch: 43 Train Loss: 0.4176 Acc: 0.8157 Pre: 0.8607 Recall: 0.7532 F1: 0.8034 Train AUC: 0.8844 Val AUC: 0.8820 Val PRC: 0.9067 Time: 0.78\n",
      "Epoch: 44 Train Loss: 0.4148 Acc: 0.8214 Pre: 0.8964 Recall: 0.7269 F1: 0.8028 Train AUC: 0.8847 Val AUC: 0.8784 Val PRC: 0.9038 Time: 0.78\n",
      "Epoch: 45 Train Loss: 0.4278 Acc: 0.8083 Pre: 0.8393 Recall: 0.7626 F1: 0.7991 Train AUC: 0.8815 Val AUC: 0.8808 Val PRC: 0.9034 Time: 0.72\n",
      "Epoch: 46 Train Loss: 0.4084 Acc: 0.8267 Pre: 0.9049 Recall: 0.7300 F1: 0.8081 Train AUC: 0.8925 Val AUC: 0.8850 Val PRC: 0.9073 Time: 0.70\n",
      "Epoch: 47 Train Loss: 0.4077 Acc: 0.8162 Pre: 0.8592 Recall: 0.7563 F1: 0.8045 Train AUC: 0.8886 Val AUC: 0.8868 Val PRC: 0.9076 Time: 0.69\n",
      "Epoch: 48 Train Loss: 0.4003 Acc: 0.8262 Pre: 0.8876 Recall: 0.7468 F1: 0.8112 Train AUC: 0.8925 Val AUC: 0.8903 Val PRC: 0.9110 Time: 0.72\n",
      "Epoch: 49 Train Loss: 0.4034 Acc: 0.8267 Pre: 0.8821 Recall: 0.7542 F1: 0.8131 Train AUC: 0.8913 Val AUC: 0.8862 Val PRC: 0.9095 Time: 0.70\n",
      "Epoch: 50 Train Loss: 0.3966 Acc: 0.8204 Pre: 0.8784 Recall: 0.7437 F1: 0.8055 Train AUC: 0.8959 Val AUC: 0.8896 Val PRC: 0.9102 Time: 0.71\n",
      "Epoch: 51 Train Loss: 0.3841 Acc: 0.8220 Pre: 0.8585 Recall: 0.7710 F1: 0.8124 Train AUC: 0.9032 Val AUC: 0.8938 Val PRC: 0.9134 Time: 0.74\n",
      "Epoch: 52 Train Loss: 0.3892 Acc: 0.8314 Pre: 0.8989 Recall: 0.7468 F1: 0.8158 Train AUC: 0.9005 Val AUC: 0.8957 Val PRC: 0.9164 Time: 0.71\n",
      "Epoch: 53 Train Loss: 0.3814 Acc: 0.8367 Pre: 0.9245 Recall: 0.7332 F1: 0.8178 Train AUC: 0.9028 Val AUC: 0.8993 Val PRC: 0.9183 Time: 0.71\n",
      "Epoch: 54 Train Loss: 0.3803 Acc: 0.8346 Pre: 0.8889 Recall: 0.7647 F1: 0.8221 Train AUC: 0.9053 Val AUC: 0.9000 Val PRC: 0.9197 Time: 0.72\n",
      "Epoch: 55 Train Loss: 0.3815 Acc: 0.8262 Pre: 0.8598 Recall: 0.7794 F1: 0.8176 Train AUC: 0.9056 Val AUC: 0.9025 Val PRC: 0.9200 Time: 0.71\n",
      "Epoch: 56 Train Loss: 0.3650 Acc: 0.8335 Pre: 0.8793 Recall: 0.7731 F1: 0.8228 Train AUC: 0.9120 Val AUC: 0.9057 Val PRC: 0.9232 Time: 0.70\n",
      "Epoch: 57 Train Loss: 0.3704 Acc: 0.8414 Pre: 0.8993 Recall: 0.7689 F1: 0.8290 Train AUC: 0.9104 Val AUC: 0.9052 Val PRC: 0.9232 Time: 0.72\n",
      "Epoch: 58 Train Loss: 0.3631 Acc: 0.8361 Pre: 0.8980 Recall: 0.7584 F1: 0.8223 Train AUC: 0.9134 Val AUC: 0.9059 Val PRC: 0.9232 Time: 0.72\n",
      "Epoch: 59 Train Loss: 0.3639 Acc: 0.8587 Pre: 0.9296 Recall: 0.7763 F1: 0.8460 Train AUC: 0.9156 Val AUC: 0.9138 Val PRC: 0.9299 Time: 0.72\n",
      "Epoch: 60 Train Loss: 0.3545 Acc: 0.8503 Pre: 0.9082 Recall: 0.7794 F1: 0.8389 Train AUC: 0.9203 Val AUC: 0.9109 Val PRC: 0.9281 Time: 0.73\n",
      "Epoch: 61 Train Loss: 0.3483 Acc: 0.8535 Pre: 0.9059 Recall: 0.7889 F1: 0.8433 Train AUC: 0.9239 Val AUC: 0.9151 Val PRC: 0.9303 Time: 0.72\n",
      "Epoch: 62 Train Loss: 0.3653 Acc: 0.8540 Pre: 0.9171 Recall: 0.7784 F1: 0.8420 Train AUC: 0.9144 Val AUC: 0.9148 Val PRC: 0.9298 Time: 0.71\n",
      "Epoch: 63 Train Loss: 0.3520 Acc: 0.8456 Pre: 0.8889 Recall: 0.7899 F1: 0.8365 Train AUC: 0.9217 Val AUC: 0.9174 Val PRC: 0.9317 Time: 0.72\n",
      "Epoch: 64 Train Loss: 0.3495 Acc: 0.8571 Pre: 0.9000 Recall: 0.8036 F1: 0.8491 Train AUC: 0.9248 Val AUC: 0.9187 Val PRC: 0.9335 Time: 0.73\n",
      "Epoch: 65 Train Loss: 0.3507 Acc: 0.8545 Pre: 0.9071 Recall: 0.7899 F1: 0.8445 Train AUC: 0.9214 Val AUC: 0.9177 Val PRC: 0.9331 Time: 0.72\n",
      "Epoch: 66 Train Loss: 0.3366 Acc: 0.8566 Pre: 0.8889 Recall: 0.8151 F1: 0.8504 Train AUC: 0.9283 Val AUC: 0.9198 Val PRC: 0.9353 Time: 0.71\n",
      "Epoch: 67 Train Loss: 0.3461 Acc: 0.8634 Pre: 0.9261 Recall: 0.7899 F1: 0.8526 Train AUC: 0.9251 Val AUC: 0.9252 Val PRC: 0.9371 Time: 0.70\n",
      "Epoch: 68 Train Loss: 0.3352 Acc: 0.8613 Pre: 0.9028 Recall: 0.8099 F1: 0.8538 Train AUC: 0.9284 Val AUC: 0.9219 Val PRC: 0.9359 Time: 0.73\n",
      "Epoch: 69 Train Loss: 0.3415 Acc: 0.8619 Pre: 0.9156 Recall: 0.7973 F1: 0.8523 Train AUC: 0.9280 Val AUC: 0.9255 Val PRC: 0.9385 Time: 0.74\n",
      "Epoch: 70 Train Loss: 0.3439 Acc: 0.8608 Pre: 0.9008 Recall: 0.8109 F1: 0.8535 Train AUC: 0.9262 Val AUC: 0.9237 Val PRC: 0.9372 Time: 0.74\n",
      "Epoch: 71 Train Loss: 0.3331 Acc: 0.8550 Pre: 0.9014 Recall: 0.7973 F1: 0.8462 Train AUC: 0.9300 Val AUC: 0.9231 Val PRC: 0.9370 Time: 0.72\n",
      "Epoch: 72 Train Loss: 0.3302 Acc: 0.8645 Pre: 0.9073 Recall: 0.8120 F1: 0.8570 Train AUC: 0.9325 Val AUC: 0.9269 Val PRC: 0.9379 Time: 0.74\n",
      "Epoch: 73 Train Loss: 0.3272 Acc: 0.8613 Pre: 0.9028 Recall: 0.8099 F1: 0.8538 Train AUC: 0.9342 Val AUC: 0.9303 Val PRC: 0.9410 Time: 0.72\n",
      "Epoch: 74 Train Loss: 0.3221 Acc: 0.8671 Pre: 0.9013 Recall: 0.8246 F1: 0.8612 Train AUC: 0.9361 Val AUC: 0.9300 Val PRC: 0.9408 Time: 0.72\n",
      "Epoch: 75 Train Loss: 0.3220 Acc: 0.8671 Pre: 0.9050 Recall: 0.8204 F1: 0.8606 Train AUC: 0.9356 Val AUC: 0.9296 Val PRC: 0.9414 Time: 0.73\n",
      "Epoch: 76 Train Loss: 0.3200 Acc: 0.8661 Pre: 0.8817 Recall: 0.8456 F1: 0.8633 Train AUC: 0.9369 Val AUC: 0.9339 Val PRC: 0.9432 Time: 0.71\n",
      "Epoch: 77 Train Loss: 0.3196 Acc: 0.8671 Pre: 0.8836 Recall: 0.8456 F1: 0.8642 Train AUC: 0.9365 Val AUC: 0.9336 Val PRC: 0.9423 Time: 0.71\n",
      "Epoch: 78 Train Loss: 0.3185 Acc: 0.8713 Pre: 0.8880 Recall: 0.8498 F1: 0.8685 Train AUC: 0.9380 Val AUC: 0.9354 Val PRC: 0.9455 Time: 0.71\n",
      "Epoch: 79 Train Loss: 0.3123 Acc: 0.8729 Pre: 0.9109 Recall: 0.8267 F1: 0.8667 Train AUC: 0.9398 Val AUC: 0.9350 Val PRC: 0.9452 Time: 0.73\n",
      "Epoch: 80 Train Loss: 0.3082 Acc: 0.8739 Pre: 0.9073 Recall: 0.8330 F1: 0.8686 Train AUC: 0.9418 Val AUC: 0.9365 Val PRC: 0.9463 Time: 0.71\n",
      "Epoch: 81 Train Loss: 0.3119 Acc: 0.8708 Pre: 0.9143 Recall: 0.8183 F1: 0.8636 Train AUC: 0.9405 Val AUC: 0.9346 Val PRC: 0.9443 Time: 0.70\n",
      "Epoch: 82 Train Loss: 0.3070 Acc: 0.8724 Pre: 0.8961 Recall: 0.8424 F1: 0.8684 Train AUC: 0.9425 Val AUC: 0.9365 Val PRC: 0.9438 Time: 0.70\n",
      "Epoch: 83 Train Loss: 0.3060 Acc: 0.8739 Pre: 0.9073 Recall: 0.8330 F1: 0.8686 Train AUC: 0.9433 Val AUC: 0.9375 Val PRC: 0.9450 Time: 0.71\n",
      "Epoch: 84 Train Loss: 0.3131 Acc: 0.8718 Pre: 0.9165 Recall: 0.8183 F1: 0.8646 Train AUC: 0.9398 Val AUC: 0.9379 Val PRC: 0.9463 Time: 0.71\n",
      "Epoch: 85 Train Loss: 0.2969 Acc: 0.8745 Pre: 0.9219 Recall: 0.8183 F1: 0.8670 Train AUC: 0.9475 Val AUC: 0.9367 Val PRC: 0.9461 Time: 0.71\n",
      "Epoch: 86 Train Loss: 0.3121 Acc: 0.8666 Pre: 0.8921 Recall: 0.8340 F1: 0.8621 Train AUC: 0.9413 Val AUC: 0.9337 Val PRC: 0.9430 Time: 0.72\n",
      "Epoch: 87 Train Loss: 0.2929 Acc: 0.8703 Pre: 0.8861 Recall: 0.8498 F1: 0.8676 Train AUC: 0.9492 Val AUC: 0.9386 Val PRC: 0.9472 Time: 0.71\n",
      "Epoch: 88 Train Loss: 0.3040 Acc: 0.8718 Pre: 0.8933 Recall: 0.8445 F1: 0.8683 Train AUC: 0.9443 Val AUC: 0.9397 Val PRC: 0.9477 Time: 0.71\n",
      "Epoch: 89 Train Loss: 0.3117 Acc: 0.8687 Pre: 0.8750 Recall: 0.8603 F1: 0.8676 Train AUC: 0.9415 Val AUC: 0.9410 Val PRC: 0.9474 Time: 0.70\n",
      "Epoch: 90 Train Loss: 0.2927 Acc: 0.8650 Pre: 0.8564 Recall: 0.8771 F1: 0.8666 Train AUC: 0.9483 Val AUC: 0.9406 Val PRC: 0.9470 Time: 0.72\n",
      "Epoch: 91 Train Loss: 0.2882 Acc: 0.8718 Pre: 0.8831 Recall: 0.8571 F1: 0.8699 Train AUC: 0.9494 Val AUC: 0.9445 Val PRC: 0.9496 Time: 0.72\n",
      "Epoch: 92 Train Loss: 0.2962 Acc: 0.8787 Pre: 0.8983 Recall: 0.8540 F1: 0.8756 Train AUC: 0.9478 Val AUC: 0.9453 Val PRC: 0.9496 Time: 0.72\n",
      "Epoch: 93 Train Loss: 0.2889 Acc: 0.8797 Pre: 0.8994 Recall: 0.8550 F1: 0.8767 Train AUC: 0.9504 Val AUC: 0.9465 Val PRC: 0.9516 Time: 0.73\n",
      "Epoch: 94 Train Loss: 0.2843 Acc: 0.8792 Pre: 0.8824 Recall: 0.8750 F1: 0.8787 Train AUC: 0.9518 Val AUC: 0.9502 Val PRC: 0.9551 Time: 0.73\n",
      "Epoch: 95 Train Loss: 0.2733 Acc: 0.8797 Pre: 0.9160 Recall: 0.8361 F1: 0.8742 Train AUC: 0.9545 Val AUC: 0.9453 Val PRC: 0.9516 Time: 0.70\n",
      "Epoch: 96 Train Loss: 0.2820 Acc: 0.8813 Pre: 0.9153 Recall: 0.8403 F1: 0.8762 Train AUC: 0.9524 Val AUC: 0.9483 Val PRC: 0.9518 Time: 0.71\n",
      "Epoch: 97 Train Loss: 0.2860 Acc: 0.8734 Pre: 0.8730 Recall: 0.8739 F1: 0.8735 Train AUC: 0.9508 Val AUC: 0.9463 Val PRC: 0.9502 Time: 0.71\n",
      "Epoch: 98 Train Loss: 0.2773 Acc: 0.8881 Pre: 0.9021 Recall: 0.8708 F1: 0.8862 Train AUC: 0.9539 Val AUC: 0.9510 Val PRC: 0.9535 Time: 0.69\n",
      "Epoch: 99 Train Loss: 0.2779 Acc: 0.8766 Pre: 0.8484 Recall: 0.9170 F1: 0.8814 Train AUC: 0.9546 Val AUC: 0.9537 Val PRC: 0.9565 Time: 0.75\n",
      "Epoch: 100 Train Loss: 0.2686 Acc: 0.8897 Pre: 0.8998 Recall: 0.8771 F1: 0.8883 Train AUC: 0.9578 Val AUC: 0.9527 Val PRC: 0.9558 Time: 0.75\n",
      "Epoch: 101 Train Loss: 0.2736 Acc: 0.8818 Pre: 0.8822 Recall: 0.8813 F1: 0.8818 Train AUC: 0.9563 Val AUC: 0.9538 Val PRC: 0.9576 Time: 0.70\n",
      "Epoch: 102 Train Loss: 0.2727 Acc: 0.8876 Pre: 0.9165 Recall: 0.8529 F1: 0.8836 Train AUC: 0.9561 Val AUC: 0.9551 Val PRC: 0.9585 Time: 0.72\n",
      "Epoch: 103 Train Loss: 0.2665 Acc: 0.8860 Pre: 0.8930 Recall: 0.8771 F1: 0.8850 Train AUC: 0.9580 Val AUC: 0.9545 Val PRC: 0.9585 Time: 0.73\n",
      "Epoch: 104 Train Loss: 0.2636 Acc: 0.8845 Pre: 0.8902 Recall: 0.8771 F1: 0.8836 Train AUC: 0.9587 Val AUC: 0.9548 Val PRC: 0.9591 Time: 0.71\n",
      "Epoch: 105 Train Loss: 0.2687 Acc: 0.8918 Pre: 0.9037 Recall: 0.8771 F1: 0.8902 Train AUC: 0.9571 Val AUC: 0.9564 Val PRC: 0.9595 Time: 0.72\n",
      "Epoch: 106 Train Loss: 0.2609 Acc: 0.8866 Pre: 0.8629 Recall: 0.9191 F1: 0.8901 Train AUC: 0.9597 Val AUC: 0.9596 Val PRC: 0.9623 Time: 0.72\n",
      "Epoch: 107 Train Loss: 0.2731 Acc: 0.8839 Pre: 0.8538 Recall: 0.9265 F1: 0.8887 Train AUC: 0.9556 Val AUC: 0.9581 Val PRC: 0.9604 Time: 0.70\n",
      "Epoch: 108 Train Loss: 0.2617 Acc: 0.8850 Pre: 0.8713 Recall: 0.9034 F1: 0.8871 Train AUC: 0.9600 Val AUC: 0.9572 Val PRC: 0.9612 Time: 0.70\n",
      "Epoch: 109 Train Loss: 0.2704 Acc: 0.8792 Pre: 0.8581 Recall: 0.9086 F1: 0.8827 Train AUC: 0.9578 Val AUC: 0.9553 Val PRC: 0.9589 Time: 0.72\n",
      "Epoch: 110 Train Loss: 0.2622 Acc: 0.8892 Pre: 0.9085 Recall: 0.8655 F1: 0.8865 Train AUC: 0.9593 Val AUC: 0.9551 Val PRC: 0.9589 Time: 0.70\n",
      "Epoch: 111 Train Loss: 0.2589 Acc: 0.8923 Pre: 0.9064 Recall: 0.8750 F1: 0.8904 Train AUC: 0.9603 Val AUC: 0.9586 Val PRC: 0.9612 Time: 0.71\n",
      "Epoch: 112 Train Loss: 0.2576 Acc: 0.8897 Pre: 0.8666 Recall: 0.9212 F1: 0.8931 Train AUC: 0.9615 Val AUC: 0.9609 Val PRC: 0.9639 Time: 0.71\n",
      "Epoch: 113 Train Loss: 0.2573 Acc: 0.8834 Pre: 0.8437 Recall: 0.9412 F1: 0.8898 Train AUC: 0.9611 Val AUC: 0.9600 Val PRC: 0.9629 Time: 0.71\n",
      "Epoch: 114 Train Loss: 0.2568 Acc: 0.8918 Pre: 0.9054 Recall: 0.8750 F1: 0.8900 Train AUC: 0.9602 Val AUC: 0.9589 Val PRC: 0.9617 Time: 0.72\n",
      "Epoch: 115 Train Loss: 0.2597 Acc: 0.8892 Pre: 0.9323 Recall: 0.8393 F1: 0.8834 Train AUC: 0.9603 Val AUC: 0.9565 Val PRC: 0.9600 Time: 0.71\n",
      "Epoch: 116 Train Loss: 0.2491 Acc: 0.8866 Pre: 0.8755 Recall: 0.9013 F1: 0.8882 Train AUC: 0.9642 Val AUC: 0.9591 Val PRC: 0.9617 Time: 0.70\n",
      "Epoch: 117 Train Loss: 0.2562 Acc: 0.8892 Pre: 0.8912 Recall: 0.8866 F1: 0.8889 Train AUC: 0.9609 Val AUC: 0.9590 Val PRC: 0.9614 Time: 0.71\n",
      "Epoch: 118 Train Loss: 0.2541 Acc: 0.8955 Pre: 0.9010 Recall: 0.8887 F1: 0.8948 Train AUC: 0.9613 Val AUC: 0.9608 Val PRC: 0.9626 Time: 0.70\n",
      "Epoch: 119 Train Loss: 0.2559 Acc: 0.8897 Pre: 0.8755 Recall: 0.9086 F1: 0.8918 Train AUC: 0.9611 Val AUC: 0.9607 Val PRC: 0.9635 Time: 0.72\n",
      "Epoch: 120 Train Loss: 0.2485 Acc: 0.8923 Pre: 0.8769 Recall: 0.9128 F1: 0.8945 Train AUC: 0.9629 Val AUC: 0.9621 Val PRC: 0.9634 Time: 0.72\n",
      "Epoch: 121 Train Loss: 0.2476 Acc: 0.8986 Pre: 0.8900 Recall: 0.9097 F1: 0.8997 Train AUC: 0.9631 Val AUC: 0.9620 Val PRC: 0.9636 Time: 0.74\n",
      "Epoch: 122 Train Loss: 0.2523 Acc: 0.8918 Pre: 0.9019 Recall: 0.8792 F1: 0.8904 Train AUC: 0.9624 Val AUC: 0.9609 Val PRC: 0.9636 Time: 0.72\n",
      "Epoch: 123 Train Loss: 0.2434 Acc: 0.8960 Pre: 0.8871 Recall: 0.9076 F1: 0.8972 Train AUC: 0.9651 Val AUC: 0.9632 Val PRC: 0.9646 Time: 0.72\n",
      "Epoch: 124 Train Loss: 0.2504 Acc: 0.9028 Pre: 0.9119 Recall: 0.8918 F1: 0.9018 Train AUC: 0.9625 Val AUC: 0.9637 Val PRC: 0.9652 Time: 0.70\n",
      "Epoch: 125 Train Loss: 0.2464 Acc: 0.8929 Pre: 0.8610 Recall: 0.9370 F1: 0.8974 Train AUC: 0.9638 Val AUC: 0.9636 Val PRC: 0.9650 Time: 0.72\n",
      "Epoch: 126 Train Loss: 0.2415 Acc: 0.8971 Pre: 0.8757 Recall: 0.9254 F1: 0.8999 Train AUC: 0.9646 Val AUC: 0.9653 Val PRC: 0.9664 Time: 0.71\n",
      "Epoch: 127 Train Loss: 0.2460 Acc: 0.8939 Pre: 0.8882 Recall: 0.9013 F1: 0.8947 Train AUC: 0.9632 Val AUC: 0.9638 Val PRC: 0.9657 Time: 0.71\n",
      "Epoch: 128 Train Loss: 0.2515 Acc: 0.8981 Pre: 0.9093 Recall: 0.8845 F1: 0.8967 Train AUC: 0.9619 Val AUC: 0.9637 Val PRC: 0.9638 Time: 0.75\n",
      "Epoch: 129 Train Loss: 0.2506 Acc: 0.8971 Pre: 0.8765 Recall: 0.9244 F1: 0.8998 Train AUC: 0.9628 Val AUC: 0.9637 Val PRC: 0.9653 Time: 0.73\n",
      "Epoch: 130 Train Loss: 0.2393 Acc: 0.8929 Pre: 0.8816 Recall: 0.9076 F1: 0.8944 Train AUC: 0.9661 Val AUC: 0.9638 Val PRC: 0.9666 Time: 0.71\n",
      "Epoch: 131 Train Loss: 0.2434 Acc: 0.8892 Pre: 0.8601 Recall: 0.9296 F1: 0.8935 Train AUC: 0.9640 Val AUC: 0.9648 Val PRC: 0.9655 Time: 0.74\n",
      "Epoch: 132 Train Loss: 0.2415 Acc: 0.8981 Pre: 0.8915 Recall: 0.9065 F1: 0.8990 Train AUC: 0.9653 Val AUC: 0.9643 Val PRC: 0.9652 Time: 0.71\n",
      "Epoch: 133 Train Loss: 0.2325 Acc: 0.8976 Pre: 0.8858 Recall: 0.9128 F1: 0.8991 Train AUC: 0.9676 Val AUC: 0.9633 Val PRC: 0.9640 Time: 0.72\n",
      "Epoch: 134 Train Loss: 0.2474 Acc: 0.8939 Pre: 0.8882 Recall: 0.9013 F1: 0.8947 Train AUC: 0.9632 Val AUC: 0.9632 Val PRC: 0.9650 Time: 0.71\n",
      "Epoch: 135 Train Loss: 0.2410 Acc: 0.8913 Pre: 0.8721 Recall: 0.9170 F1: 0.8940 Train AUC: 0.9648 Val AUC: 0.9638 Val PRC: 0.9649 Time: 0.72\n",
      "Epoch: 136 Train Loss: 0.2338 Acc: 0.8876 Pre: 0.8514 Recall: 0.9391 F1: 0.8931 Train AUC: 0.9681 Val AUC: 0.9637 Val PRC: 0.9652 Time: 0.72\n",
      "Epoch: 137 Train Loss: 0.2329 Acc: 0.9013 Pre: 0.8835 Recall: 0.9244 F1: 0.9035 Train AUC: 0.9671 Val AUC: 0.9651 Val PRC: 0.9660 Time: 0.72\n",
      "Epoch: 138 Train Loss: 0.2291 Acc: 0.9018 Pre: 0.8989 Recall: 0.9055 F1: 0.9021 Train AUC: 0.9679 Val AUC: 0.9676 Val PRC: 0.9690 Time: 0.70\n",
      "Epoch: 139 Train Loss: 0.2264 Acc: 0.8992 Pre: 0.9086 Recall: 0.8876 F1: 0.8980 Train AUC: 0.9691 Val AUC: 0.9680 Val PRC: 0.9694 Time: 0.71\n",
      "Epoch: 140 Train Loss: 0.2386 Acc: 0.9002 Pre: 0.8952 Recall: 0.9065 F1: 0.9008 Train AUC: 0.9651 Val AUC: 0.9653 Val PRC: 0.9663 Time: 0.71\n",
      "Epoch: 141 Train Loss: 0.2186 Acc: 0.9034 Pre: 0.8943 Recall: 0.9149 F1: 0.9045 Train AUC: 0.9717 Val AUC: 0.9674 Val PRC: 0.9687 Time: 0.70\n",
      "Epoch: 142 Train Loss: 0.2283 Acc: 0.9049 Pre: 0.9269 Recall: 0.8792 F1: 0.9024 Train AUC: 0.9685 Val AUC: 0.9658 Val PRC: 0.9660 Time: 0.70\n",
      "Epoch: 143 Train Loss: 0.2310 Acc: 0.8960 Pre: 0.8748 Recall: 0.9244 F1: 0.8989 Train AUC: 0.9678 Val AUC: 0.9677 Val PRC: 0.9692 Time: 0.72\n",
      "Epoch: 144 Train Loss: 0.2275 Acc: 0.9081 Pre: 0.9164 Recall: 0.8981 F1: 0.9072 Train AUC: 0.9687 Val AUC: 0.9660 Val PRC: 0.9674 Time: 0.70\n",
      "Epoch: 145 Train Loss: 0.2176 Acc: 0.9060 Pre: 0.9030 Recall: 0.9097 F1: 0.9063 Train AUC: 0.9714 Val AUC: 0.9686 Val PRC: 0.9698 Time: 0.71\n",
      "Epoch: 146 Train Loss: 0.2200 Acc: 0.8981 Pre: 0.8730 Recall: 0.9317 F1: 0.9014 Train AUC: 0.9707 Val AUC: 0.9680 Val PRC: 0.9691 Time: 0.74\n",
      "Epoch: 147 Train Loss: 0.2238 Acc: 0.9023 Pre: 0.8981 Recall: 0.9076 F1: 0.9028 Train AUC: 0.9696 Val AUC: 0.9666 Val PRC: 0.9679 Time: 0.72\n",
      "Epoch: 148 Train Loss: 0.2203 Acc: 0.9013 Pre: 0.8963 Recall: 0.9076 F1: 0.9019 Train AUC: 0.9706 Val AUC: 0.9671 Val PRC: 0.9680 Time: 0.71\n",
      "Epoch: 149 Train Loss: 0.2198 Acc: 0.9039 Pre: 0.9001 Recall: 0.9086 F1: 0.9043 Train AUC: 0.9706 Val AUC: 0.9663 Val PRC: 0.9675 Time: 0.72\n",
      "Epoch: 150 Train Loss: 0.2144 Acc: 0.9028 Pre: 0.8941 Recall: 0.9139 F1: 0.9039 Train AUC: 0.9719 Val AUC: 0.9683 Val PRC: 0.9690 Time: 0.71\n",
      "Epoch: 151 Train Loss: 0.2203 Acc: 0.9034 Pre: 0.8871 Recall: 0.9244 F1: 0.9053 Train AUC: 0.9706 Val AUC: 0.9685 Val PRC: 0.9699 Time: 0.71\n",
      "Epoch: 152 Train Loss: 0.2162 Acc: 0.9070 Pre: 0.9144 Recall: 0.8981 F1: 0.9062 Train AUC: 0.9716 Val AUC: 0.9703 Val PRC: 0.9715 Time: 0.72\n",
      "Epoch: 153 Train Loss: 0.2276 Acc: 0.9002 Pre: 0.8912 Recall: 0.9118 F1: 0.9014 Train AUC: 0.9685 Val AUC: 0.9684 Val PRC: 0.9698 Time: 0.72\n",
      "Epoch: 154 Train Loss: 0.2212 Acc: 0.9049 Pre: 0.9054 Recall: 0.9044 F1: 0.9049 Train AUC: 0.9704 Val AUC: 0.9695 Val PRC: 0.9707 Time: 0.70\n",
      "Epoch: 155 Train Loss: 0.2162 Acc: 0.9044 Pre: 0.8945 Recall: 0.9170 F1: 0.9056 Train AUC: 0.9713 Val AUC: 0.9693 Val PRC: 0.9695 Time: 0.72\n",
      "Epoch: 156 Train Loss: 0.2151 Acc: 0.9028 Pre: 0.8901 Recall: 0.9191 F1: 0.9044 Train AUC: 0.9719 Val AUC: 0.9688 Val PRC: 0.9694 Time: 0.70\n",
      "Epoch: 157 Train Loss: 0.2154 Acc: 0.9060 Pre: 0.9151 Recall: 0.8950 F1: 0.9049 Train AUC: 0.9714 Val AUC: 0.9692 Val PRC: 0.9699 Time: 0.71\n",
      "Epoch: 158 Train Loss: 0.2095 Acc: 0.9023 Pre: 0.8792 Recall: 0.9328 F1: 0.9052 Train AUC: 0.9731 Val AUC: 0.9687 Val PRC: 0.9697 Time: 0.72\n",
      "Epoch: 159 Train Loss: 0.2146 Acc: 0.9065 Pre: 0.8941 Recall: 0.9223 F1: 0.9080 Train AUC: 0.9715 Val AUC: 0.9705 Val PRC: 0.9714 Time: 0.74\n",
      "Epoch: 160 Train Loss: 0.2153 Acc: 0.9007 Pre: 0.9012 Recall: 0.9002 F1: 0.9007 Train AUC: 0.9714 Val AUC: 0.9688 Val PRC: 0.9695 Time: 0.93\n",
      "Epoch: 161 Train Loss: 0.2165 Acc: 0.9091 Pre: 0.8979 Recall: 0.9233 F1: 0.9104 Train AUC: 0.9705 Val AUC: 0.9721 Val PRC: 0.9729 Time: 0.71\n",
      "Epoch: 162 Train Loss: 0.2113 Acc: 0.9133 Pre: 0.9191 Recall: 0.9065 F1: 0.9127 Train AUC: 0.9726 Val AUC: 0.9712 Val PRC: 0.9708 Time: 0.74\n",
      "Epoch: 163 Train Loss: 0.2121 Acc: 0.9107 Pre: 0.9204 Recall: 0.8992 F1: 0.9097 Train AUC: 0.9722 Val AUC: 0.9710 Val PRC: 0.9708 Time: 0.71\n",
      "Epoch: 164 Train Loss: 0.2150 Acc: 0.9123 Pre: 0.9017 Recall: 0.9254 F1: 0.9134 Train AUC: 0.9719 Val AUC: 0.9714 Val PRC: 0.9716 Time: 0.72\n",
      "Epoch: 165 Train Loss: 0.2113 Acc: 0.9133 Pre: 0.8987 Recall: 0.9317 F1: 0.9149 Train AUC: 0.9734 Val AUC: 0.9720 Val PRC: 0.9722 Time: 0.71\n",
      "Epoch: 166 Train Loss: 0.2133 Acc: 0.9097 Pre: 0.8947 Recall: 0.9286 F1: 0.9113 Train AUC: 0.9720 Val AUC: 0.9710 Val PRC: 0.9712 Time: 0.71\n",
      "Epoch: 167 Train Loss: 0.2094 Acc: 0.9102 Pre: 0.9141 Recall: 0.9055 F1: 0.9098 Train AUC: 0.9735 Val AUC: 0.9692 Val PRC: 0.9693 Time: 0.71\n",
      "Epoch: 168 Train Loss: 0.2151 Acc: 0.9070 Pre: 0.8999 Recall: 0.9160 F1: 0.9079 Train AUC: 0.9718 Val AUC: 0.9718 Val PRC: 0.9721 Time: 0.70\n",
      "Epoch: 169 Train Loss: 0.2150 Acc: 0.9081 Pre: 0.8968 Recall: 0.9223 F1: 0.9094 Train AUC: 0.9723 Val AUC: 0.9719 Val PRC: 0.9727 Time: 0.71\n",
      "Epoch: 170 Train Loss: 0.2082 Acc: 0.9060 Pre: 0.9014 Recall: 0.9118 F1: 0.9065 Train AUC: 0.9737 Val AUC: 0.9704 Val PRC: 0.9711 Time: 0.71\n",
      "Epoch: 171 Train Loss: 0.2074 Acc: 0.9039 Pre: 0.8729 Recall: 0.9454 F1: 0.9077 Train AUC: 0.9735 Val AUC: 0.9716 Val PRC: 0.9718 Time: 0.71\n",
      "Epoch: 172 Train Loss: 0.2056 Acc: 0.9076 Pre: 0.9128 Recall: 0.9013 F1: 0.9070 Train AUC: 0.9742 Val AUC: 0.9704 Val PRC: 0.9714 Time: 0.71\n",
      "Epoch: 173 Train Loss: 0.2059 Acc: 0.9091 Pre: 0.9104 Recall: 0.9076 F1: 0.9090 Train AUC: 0.9736 Val AUC: 0.9712 Val PRC: 0.9719 Time: 0.69\n",
      "Epoch: 174 Train Loss: 0.2064 Acc: 0.9076 Pre: 0.8984 Recall: 0.9191 F1: 0.9086 Train AUC: 0.9737 Val AUC: 0.9695 Val PRC: 0.9684 Time: 0.72\n",
      "Epoch: 175 Train Loss: 0.2047 Acc: 0.9107 Pre: 0.9056 Recall: 0.9170 F1: 0.9113 Train AUC: 0.9742 Val AUC: 0.9725 Val PRC: 0.9729 Time: 0.70\n",
      "Epoch: 176 Train Loss: 0.2071 Acc: 0.9013 Pre: 0.8680 Recall: 0.9464 F1: 0.9055 Train AUC: 0.9741 Val AUC: 0.9720 Val PRC: 0.9726 Time: 0.71\n",
      "Epoch: 177 Train Loss: 0.2142 Acc: 0.9154 Pre: 0.9133 Recall: 0.9181 F1: 0.9157 Train AUC: 0.9749 Val AUC: 0.9735 Val PRC: 0.9712 Time: 0.73\n",
      "Epoch: 178 Train Loss: 0.1965 Acc: 0.9118 Pre: 0.8944 Recall: 0.9338 F1: 0.9137 Train AUC: 0.9762 Val AUC: 0.9729 Val PRC: 0.9731 Time: 0.70\n",
      "Epoch: 179 Train Loss: 0.2016 Acc: 0.9065 Pre: 0.9006 Recall: 0.9139 F1: 0.9072 Train AUC: 0.9746 Val AUC: 0.9707 Val PRC: 0.9711 Time: 0.70\n",
      "Epoch: 180 Train Loss: 0.1903 Acc: 0.9102 Pre: 0.8948 Recall: 0.9296 F1: 0.9119 Train AUC: 0.9781 Val AUC: 0.9735 Val PRC: 0.9741 Time: 0.73\n",
      "Epoch: 181 Train Loss: 0.2033 Acc: 0.9112 Pre: 0.9196 Recall: 0.9013 F1: 0.9103 Train AUC: 0.9749 Val AUC: 0.9725 Val PRC: 0.9735 Time: 0.71\n",
      "Epoch: 182 Train Loss: 0.2033 Acc: 0.9102 Pre: 0.9106 Recall: 0.9097 F1: 0.9101 Train AUC: 0.9744 Val AUC: 0.9713 Val PRC: 0.9722 Time: 0.73\n",
      "Epoch: 183 Train Loss: 0.1962 Acc: 0.9076 Pre: 0.8872 Recall: 0.9338 F1: 0.9099 Train AUC: 0.9761 Val AUC: 0.9709 Val PRC: 0.9709 Time: 0.72\n",
      "Epoch: 184 Train Loss: 0.1930 Acc: 0.9097 Pre: 0.8939 Recall: 0.9296 F1: 0.9114 Train AUC: 0.9769 Val AUC: 0.9724 Val PRC: 0.9729 Time: 0.72\n",
      "Epoch: 185 Train Loss: 0.1963 Acc: 0.9070 Pre: 0.8773 Recall: 0.9464 F1: 0.9106 Train AUC: 0.9756 Val AUC: 0.9724 Val PRC: 0.9724 Time: 0.71\n",
      "Epoch: 186 Train Loss: 0.1987 Acc: 0.9070 Pre: 0.8871 Recall: 0.9328 F1: 0.9094 Train AUC: 0.9757 Val AUC: 0.9727 Val PRC: 0.9729 Time: 0.72\n",
      "Epoch: 187 Train Loss: 0.1920 Acc: 0.9091 Pre: 0.8742 Recall: 0.9559 F1: 0.9132 Train AUC: 0.9770 Val AUC: 0.9738 Val PRC: 0.9740 Time: 0.71\n",
      "Epoch: 188 Train Loss: 0.1912 Acc: 0.9170 Pre: 0.9068 Recall: 0.9296 F1: 0.9180 Train AUC: 0.9769 Val AUC: 0.9733 Val PRC: 0.9727 Time: 0.72\n",
      "Epoch: 189 Train Loss: 0.1854 Acc: 0.9133 Pre: 0.9191 Recall: 0.9065 F1: 0.9127 Train AUC: 0.9785 Val AUC: 0.9749 Val PRC: 0.9758 Time: 0.71\n",
      "Epoch: 190 Train Loss: 0.1930 Acc: 0.9191 Pre: 0.9097 Recall: 0.9307 F1: 0.9200 Train AUC: 0.9766 Val AUC: 0.9748 Val PRC: 0.9757 Time: 0.73\n",
      "Epoch: 191 Train Loss: 0.1789 Acc: 0.9102 Pre: 0.8847 Recall: 0.9433 F1: 0.9131 Train AUC: 0.9804 Val AUC: 0.9737 Val PRC: 0.9737 Time: 0.72\n",
      "Epoch: 192 Train Loss: 0.1825 Acc: 0.9060 Pre: 0.8699 Recall: 0.9548 F1: 0.9104 Train AUC: 0.9791 Val AUC: 0.9738 Val PRC: 0.9742 Time: 0.73\n",
      "Epoch: 193 Train Loss: 0.1911 Acc: 0.9144 Pre: 0.9054 Recall: 0.9254 F1: 0.9153 Train AUC: 0.9773 Val AUC: 0.9721 Val PRC: 0.9729 Time: 0.71\n",
      "Epoch: 194 Train Loss: 0.1907 Acc: 0.9144 Pre: 0.9148 Recall: 0.9139 F1: 0.9143 Train AUC: 0.9767 Val AUC: 0.9740 Val PRC: 0.9751 Time: 0.70\n",
      "Epoch: 195 Train Loss: 0.1992 Acc: 0.9133 Pre: 0.8955 Recall: 0.9359 F1: 0.9153 Train AUC: 0.9749 Val AUC: 0.9756 Val PRC: 0.9761 Time: 0.71\n",
      "Epoch: 196 Train Loss: 0.1874 Acc: 0.9144 Pre: 0.9175 Recall: 0.9107 F1: 0.9141 Train AUC: 0.9778 Val AUC: 0.9749 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 197 Train Loss: 0.1865 Acc: 0.9139 Pre: 0.9053 Recall: 0.9244 F1: 0.9148 Train AUC: 0.9777 Val AUC: 0.9759 Val PRC: 0.9755 Time: 0.72\n",
      "Epoch: 198 Train Loss: 0.1828 Acc: 0.9170 Pre: 0.8839 Recall: 0.9601 F1: 0.9204 Train AUC: 0.9791 Val AUC: 0.9765 Val PRC: 0.9734 Time: 0.72\n",
      "Epoch: 199 Train Loss: 0.1824 Acc: 0.9144 Pre: 0.9175 Recall: 0.9107 F1: 0.9141 Train AUC: 0.9792 Val AUC: 0.9764 Val PRC: 0.9767 Time: 0.71\n",
      "Epoch: 200 Train Loss: 0.2012 Acc: 0.9165 Pre: 0.8985 Recall: 0.9391 F1: 0.9183 Train AUC: 0.9771 Val AUC: 0.9744 Val PRC: 0.9706 Time: 0.72\n",
      "Epoch: 201 Train Loss: 0.1886 Acc: 0.9133 Pre: 0.8915 Recall: 0.9412 F1: 0.9157 Train AUC: 0.9777 Val AUC: 0.9767 Val PRC: 0.9766 Time: 0.71\n",
      "Epoch: 202 Train Loss: 0.1782 Acc: 0.9196 Pre: 0.9089 Recall: 0.9328 F1: 0.9207 Train AUC: 0.9804 Val AUC: 0.9767 Val PRC: 0.9765 Time: 0.69\n",
      "Epoch: 203 Train Loss: 0.1872 Acc: 0.9123 Pre: 0.8993 Recall: 0.9286 F1: 0.9137 Train AUC: 0.9776 Val AUC: 0.9753 Val PRC: 0.9754 Time: 0.70\n",
      "Epoch: 204 Train Loss: 0.1773 Acc: 0.9186 Pre: 0.9138 Recall: 0.9244 F1: 0.9191 Train AUC: 0.9802 Val AUC: 0.9760 Val PRC: 0.9762 Time: 0.70\n",
      "Epoch: 205 Train Loss: 0.1822 Acc: 0.9149 Pre: 0.9247 Recall: 0.9034 F1: 0.9139 Train AUC: 0.9792 Val AUC: 0.9745 Val PRC: 0.9740 Time: 0.71\n",
      "Epoch: 206 Train Loss: 0.1877 Acc: 0.9065 Pre: 0.8637 Recall: 0.9653 F1: 0.9117 Train AUC: 0.9781 Val AUC: 0.9743 Val PRC: 0.9739 Time: 0.70\n",
      "Epoch: 207 Train Loss: 0.1849 Acc: 0.9128 Pre: 0.8816 Recall: 0.9538 F1: 0.9162 Train AUC: 0.9783 Val AUC: 0.9758 Val PRC: 0.9760 Time: 0.70\n",
      "Epoch: 208 Train Loss: 0.1780 Acc: 0.9154 Pre: 0.9124 Recall: 0.9191 F1: 0.9158 Train AUC: 0.9794 Val AUC: 0.9759 Val PRC: 0.9756 Time: 0.72\n",
      "Epoch: 209 Train Loss: 0.1776 Acc: 0.9175 Pre: 0.9069 Recall: 0.9307 F1: 0.9186 Train AUC: 0.9797 Val AUC: 0.9747 Val PRC: 0.9736 Time: 0.70\n",
      "Epoch: 210 Train Loss: 0.1696 Acc: 0.9165 Pre: 0.8922 Recall: 0.9475 F1: 0.9190 Train AUC: 0.9816 Val AUC: 0.9753 Val PRC: 0.9743 Time: 0.72\n",
      "Epoch: 211 Train Loss: 0.1861 Acc: 0.9154 Pre: 0.9065 Recall: 0.9265 F1: 0.9164 Train AUC: 0.9777 Val AUC: 0.9752 Val PRC: 0.9751 Time: 0.74\n",
      "Epoch: 212 Train Loss: 0.1730 Acc: 0.9181 Pre: 0.8964 Recall: 0.9454 F1: 0.9202 Train AUC: 0.9809 Val AUC: 0.9762 Val PRC: 0.9763 Time: 0.73\n",
      "Epoch: 213 Train Loss: 0.1723 Acc: 0.9144 Pre: 0.9005 Recall: 0.9317 F1: 0.9158 Train AUC: 0.9811 Val AUC: 0.9768 Val PRC: 0.9769 Time: 0.72\n",
      "Epoch: 214 Train Loss: 0.1781 Acc: 0.9223 Pre: 0.9196 Recall: 0.9254 F1: 0.9225 Train AUC: 0.9796 Val AUC: 0.9766 Val PRC: 0.9761 Time: 0.72\n",
      "Epoch: 215 Train Loss: 0.1734 Acc: 0.9181 Pre: 0.9289 Recall: 0.9055 F1: 0.9170 Train AUC: 0.9805 Val AUC: 0.9766 Val PRC: 0.9774 Time: 0.70\n",
      "Epoch: 216 Train Loss: 0.1664 Acc: 0.9144 Pre: 0.8856 Recall: 0.9517 F1: 0.9175 Train AUC: 0.9827 Val AUC: 0.9766 Val PRC: 0.9765 Time: 0.71\n",
      "Epoch: 217 Train Loss: 0.1712 Acc: 0.9154 Pre: 0.9141 Recall: 0.9170 F1: 0.9156 Train AUC: 0.9810 Val AUC: 0.9745 Val PRC: 0.9736 Time: 0.74\n",
      "Epoch: 218 Train Loss: 0.1774 Acc: 0.9170 Pre: 0.9325 Recall: 0.8992 F1: 0.9155 Train AUC: 0.9799 Val AUC: 0.9778 Val PRC: 0.9779 Time: 0.74\n",
      "Epoch: 219 Train Loss: 0.1743 Acc: 0.9202 Pre: 0.9202 Recall: 0.9202 F1: 0.9202 Train AUC: 0.9806 Val AUC: 0.9771 Val PRC: 0.9773 Time: 0.72\n",
      "Epoch: 220 Train Loss: 0.1720 Acc: 0.9165 Pre: 0.8891 Recall: 0.9517 F1: 0.9193 Train AUC: 0.9812 Val AUC: 0.9769 Val PRC: 0.9771 Time: 0.74\n",
      "Epoch: 221 Train Loss: 0.1682 Acc: 0.9160 Pre: 0.8968 Recall: 0.9401 F1: 0.9179 Train AUC: 0.9820 Val AUC: 0.9787 Val PRC: 0.9789 Time: 0.72\n",
      "Epoch: 222 Train Loss: 0.1688 Acc: 0.9186 Pre: 0.9046 Recall: 0.9359 F1: 0.9200 Train AUC: 0.9816 Val AUC: 0.9780 Val PRC: 0.9780 Time: 0.72\n",
      "Epoch: 223 Train Loss: 0.1645 Acc: 0.9175 Pre: 0.9027 Recall: 0.9359 F1: 0.9190 Train AUC: 0.9825 Val AUC: 0.9771 Val PRC: 0.9768 Time: 0.72\n",
      "Epoch: 224 Train Loss: 0.1698 Acc: 0.9217 Pre: 0.8963 Recall: 0.9538 F1: 0.9242 Train AUC: 0.9815 Val AUC: 0.9780 Val PRC: 0.9774 Time: 0.71\n",
      "Epoch: 225 Train Loss: 0.1670 Acc: 0.9191 Pre: 0.9047 Recall: 0.9370 F1: 0.9205 Train AUC: 0.9812 Val AUC: 0.9754 Val PRC: 0.9742 Time: 0.73\n",
      "Epoch: 226 Train Loss: 0.1737 Acc: 0.9181 Pre: 0.9112 Recall: 0.9265 F1: 0.9188 Train AUC: 0.9801 Val AUC: 0.9783 Val PRC: 0.9788 Time: 0.73\n",
      "Epoch: 227 Train Loss: 0.1700 Acc: 0.9154 Pre: 0.8920 Recall: 0.9454 F1: 0.9179 Train AUC: 0.9813 Val AUC: 0.9774 Val PRC: 0.9786 Time: 0.73\n",
      "Epoch: 228 Train Loss: 0.1704 Acc: 0.9196 Pre: 0.9347 Recall: 0.9023 F1: 0.9182 Train AUC: 0.9813 Val AUC: 0.9778 Val PRC: 0.9784 Time: 0.72\n",
      "Epoch: 229 Train Loss: 0.1585 Acc: 0.9175 Pre: 0.9036 Recall: 0.9349 F1: 0.9189 Train AUC: 0.9837 Val AUC: 0.9785 Val PRC: 0.9790 Time: 0.72\n",
      "Epoch: 230 Train Loss: 0.1571 Acc: 0.9181 Pre: 0.9181 Recall: 0.9181 F1: 0.9181 Train AUC: 0.9844 Val AUC: 0.9758 Val PRC: 0.9750 Time: 0.72\n",
      "Epoch: 231 Train Loss: 0.1665 Acc: 0.9175 Pre: 0.9128 Recall: 0.9233 F1: 0.9180 Train AUC: 0.9818 Val AUC: 0.9772 Val PRC: 0.9775 Time: 0.71\n",
      "Epoch: 232 Train Loss: 0.1649 Acc: 0.9170 Pre: 0.9110 Recall: 0.9244 F1: 0.9176 Train AUC: 0.9827 Val AUC: 0.9777 Val PRC: 0.9780 Time: 0.74\n",
      "Epoch: 233 Train Loss: 0.1671 Acc: 0.9149 Pre: 0.8828 Recall: 0.9569 F1: 0.9183 Train AUC: 0.9822 Val AUC: 0.9788 Val PRC: 0.9793 Time: 0.70\n",
      "Epoch: 234 Train Loss: 0.1549 Acc: 0.9196 Pre: 0.9376 Recall: 0.8992 F1: 0.9180 Train AUC: 0.9844 Val AUC: 0.9786 Val PRC: 0.9787 Time: 0.71\n",
      "Epoch: 235 Train Loss: 0.1629 Acc: 0.9233 Pre: 0.8990 Recall: 0.9538 F1: 0.9256 Train AUC: 0.9826 Val AUC: 0.9775 Val PRC: 0.9772 Time: 0.71\n",
      "Epoch: 236 Train Loss: 0.1682 Acc: 0.9207 Pre: 0.9041 Recall: 0.9412 F1: 0.9223 Train AUC: 0.9816 Val AUC: 0.9791 Val PRC: 0.9792 Time: 0.70\n",
      "Epoch: 237 Train Loss: 0.1642 Acc: 0.9191 Pre: 0.9105 Recall: 0.9296 F1: 0.9200 Train AUC: 0.9823 Val AUC: 0.9770 Val PRC: 0.9704 Time: 0.70\n",
      "Epoch: 238 Train Loss: 0.1639 Acc: 0.9175 Pre: 0.8855 Recall: 0.9590 F1: 0.9208 Train AUC: 0.9818 Val AUC: 0.9800 Val PRC: 0.9792 Time: 0.72\n",
      "Epoch: 239 Train Loss: 0.1671 Acc: 0.9191 Pre: 0.9080 Recall: 0.9328 F1: 0.9202 Train AUC: 0.9809 Val AUC: 0.9781 Val PRC: 0.9778 Time: 0.73\n",
      "Epoch: 240 Train Loss: 0.1570 Acc: 0.9191 Pre: 0.9328 Recall: 0.9034 F1: 0.9178 Train AUC: 0.9837 Val AUC: 0.9766 Val PRC: 0.9758 Time: 0.71\n",
      "Epoch: 241 Train Loss: 0.1660 Acc: 0.9165 Pre: 0.8993 Recall: 0.9380 F1: 0.9183 Train AUC: 0.9821 Val AUC: 0.9778 Val PRC: 0.9784 Time: 0.74\n",
      "Epoch: 242 Train Loss: 0.1631 Acc: 0.9223 Pre: 0.9188 Recall: 0.9265 F1: 0.9226 Train AUC: 0.9824 Val AUC: 0.9786 Val PRC: 0.9786 Time: 0.71\n",
      "Epoch: 243 Train Loss: 0.1563 Acc: 0.9144 Pre: 0.8894 Recall: 0.9464 F1: 0.9170 Train AUC: 0.9842 Val AUC: 0.9775 Val PRC: 0.9778 Time: 0.71\n",
      "Epoch: 244 Train Loss: 0.1569 Acc: 0.9244 Pre: 0.9244 Recall: 0.9244 F1: 0.9244 Train AUC: 0.9836 Val AUC: 0.9803 Val PRC: 0.9801 Time: 0.72\n",
      "Epoch: 245 Train Loss: 0.1611 Acc: 0.9207 Pre: 0.9133 Recall: 0.9296 F1: 0.9214 Train AUC: 0.9826 Val AUC: 0.9780 Val PRC: 0.9769 Time: 0.72\n",
      "Epoch: 246 Train Loss: 0.1601 Acc: 0.9217 Pre: 0.9076 Recall: 0.9391 F1: 0.9231 Train AUC: 0.9829 Val AUC: 0.9789 Val PRC: 0.9784 Time: 0.73\n",
      "Epoch: 247 Train Loss: 0.1598 Acc: 0.9186 Pre: 0.8949 Recall: 0.9485 F1: 0.9210 Train AUC: 0.9834 Val AUC: 0.9791 Val PRC: 0.9786 Time: 0.74\n",
      "Epoch: 248 Train Loss: 0.1636 Acc: 0.9202 Pre: 0.8960 Recall: 0.9506 F1: 0.9225 Train AUC: 0.9822 Val AUC: 0.9795 Val PRC: 0.9792 Time: 0.71\n",
      "Epoch: 249 Train Loss: 0.1541 Acc: 0.9259 Pre: 0.9403 Recall: 0.9097 F1: 0.9247 Train AUC: 0.9840 Val AUC: 0.9800 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 250 Train Loss: 0.1601 Acc: 0.9223 Pre: 0.8949 Recall: 0.9569 F1: 0.9249 Train AUC: 0.9830 Val AUC: 0.9800 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 251 Train Loss: 0.1648 Acc: 0.9212 Pre: 0.9002 Recall: 0.9475 F1: 0.9232 Train AUC: 0.9813 Val AUC: 0.9774 Val PRC: 0.9767 Time: 0.72\n",
      "Epoch: 252 Train Loss: 0.1458 Acc: 0.9254 Pre: 0.9083 Recall: 0.9464 F1: 0.9270 Train AUC: 0.9864 Val AUC: 0.9801 Val PRC: 0.9805 Time: 0.70\n",
      "Epoch: 253 Train Loss: 0.1584 Acc: 0.9165 Pre: 0.9050 Recall: 0.9307 F1: 0.9177 Train AUC: 0.9835 Val AUC: 0.9775 Val PRC: 0.9779 Time: 0.72\n",
      "Epoch: 254 Train Loss: 0.1549 Acc: 0.9212 Pre: 0.9456 Recall: 0.8939 F1: 0.9190 Train AUC: 0.9841 Val AUC: 0.9792 Val PRC: 0.9787 Time: 0.72\n",
      "Epoch: 255 Train Loss: 0.1632 Acc: 0.9233 Pre: 0.8974 Recall: 0.9559 F1: 0.9257 Train AUC: 0.9826 Val AUC: 0.9788 Val PRC: 0.9780 Time: 0.70\n",
      "Epoch: 256 Train Loss: 0.1639 Acc: 0.9270 Pre: 0.9204 Recall: 0.9349 F1: 0.9276 Train AUC: 0.9819 Val AUC: 0.9787 Val PRC: 0.9774 Time: 0.71\n",
      "Epoch: 257 Train Loss: 0.1599 Acc: 0.9238 Pre: 0.9243 Recall: 0.9233 F1: 0.9238 Train AUC: 0.9827 Val AUC: 0.9804 Val PRC: 0.9798 Time: 0.71\n",
      "Epoch: 258 Train Loss: 0.1515 Acc: 0.9223 Pre: 0.9127 Recall: 0.9338 F1: 0.9232 Train AUC: 0.9850 Val AUC: 0.9803 Val PRC: 0.9802 Time: 0.70\n",
      "Epoch: 259 Train Loss: 0.1534 Acc: 0.9186 Pre: 0.8903 Recall: 0.9548 F1: 0.9214 Train AUC: 0.9851 Val AUC: 0.9794 Val PRC: 0.9796 Time: 0.71\n",
      "Epoch: 260 Train Loss: 0.1445 Acc: 0.9191 Pre: 0.9022 Recall: 0.9401 F1: 0.9208 Train AUC: 0.9863 Val AUC: 0.9786 Val PRC: 0.9780 Time: 0.73\n",
      "Epoch: 261 Train Loss: 0.1603 Acc: 0.9191 Pre: 0.8889 Recall: 0.9580 F1: 0.9221 Train AUC: 0.9827 Val AUC: 0.9797 Val PRC: 0.9798 Time: 0.72\n",
      "Epoch: 262 Train Loss: 0.1533 Acc: 0.9280 Pre: 0.9214 Recall: 0.9359 F1: 0.9286 Train AUC: 0.9841 Val AUC: 0.9804 Val PRC: 0.9798 Time: 0.72\n",
      "Epoch: 263 Train Loss: 0.1489 Acc: 0.9196 Pre: 0.9056 Recall: 0.9370 F1: 0.9210 Train AUC: 0.9853 Val AUC: 0.9804 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 264 Train Loss: 0.1636 Acc: 0.9233 Pre: 0.9315 Recall: 0.9139 F1: 0.9226 Train AUC: 0.9820 Val AUC: 0.9775 Val PRC: 0.9777 Time: 0.70\n",
      "Epoch: 265 Train Loss: 0.1497 Acc: 0.9244 Pre: 0.9271 Recall: 0.9212 F1: 0.9241 Train AUC: 0.9851 Val AUC: 0.9796 Val PRC: 0.9799 Time: 0.70\n",
      "Epoch: 266 Train Loss: 0.1412 Acc: 0.9244 Pre: 0.9191 Recall: 0.9307 F1: 0.9248 Train AUC: 0.9867 Val AUC: 0.9777 Val PRC: 0.9750 Time: 0.71\n",
      "Epoch: 267 Train Loss: 0.1448 Acc: 0.9244 Pre: 0.9262 Recall: 0.9223 F1: 0.9242 Train AUC: 0.9858 Val AUC: 0.9783 Val PRC: 0.9757 Time: 0.71\n",
      "Epoch: 268 Train Loss: 0.1508 Acc: 0.9249 Pre: 0.9132 Recall: 0.9391 F1: 0.9259 Train AUC: 0.9850 Val AUC: 0.9779 Val PRC: 0.9763 Time: 0.72\n",
      "Epoch: 269 Train Loss: 0.1371 Acc: 0.9207 Pre: 0.8985 Recall: 0.9485 F1: 0.9228 Train AUC: 0.9876 Val AUC: 0.9801 Val PRC: 0.9799 Time: 0.71\n",
      "Epoch: 270 Train Loss: 0.1525 Acc: 0.9217 Pre: 0.9195 Recall: 0.9244 F1: 0.9219 Train AUC: 0.9843 Val AUC: 0.9785 Val PRC: 0.9789 Time: 0.71\n",
      "Epoch: 271 Train Loss: 0.1587 Acc: 0.9223 Pre: 0.9020 Recall: 0.9475 F1: 0.9242 Train AUC: 0.9854 Val AUC: 0.9791 Val PRC: 0.9788 Time: 0.72\n",
      "Epoch: 272 Train Loss: 0.1498 Acc: 0.9186 Pre: 0.8880 Recall: 0.9580 F1: 0.9217 Train AUC: 0.9853 Val AUC: 0.9786 Val PRC: 0.9779 Time: 0.72\n",
      "Epoch: 273 Train Loss: 0.1504 Acc: 0.9223 Pre: 0.9052 Recall: 0.9433 F1: 0.9239 Train AUC: 0.9849 Val AUC: 0.9787 Val PRC: 0.9781 Time: 0.70\n",
      "Epoch: 274 Train Loss: 0.1411 Acc: 0.9270 Pre: 0.9274 Recall: 0.9265 F1: 0.9270 Train AUC: 0.9861 Val AUC: 0.9791 Val PRC: 0.9782 Time: 0.72\n",
      "Epoch: 275 Train Loss: 0.1420 Acc: 0.9254 Pre: 0.9133 Recall: 0.9401 F1: 0.9265 Train AUC: 0.9859 Val AUC: 0.9783 Val PRC: 0.9776 Time: 0.71\n",
      "Epoch: 276 Train Loss: 0.1385 Acc: 0.9254 Pre: 0.9034 Recall: 0.9527 F1: 0.9274 Train AUC: 0.9875 Val AUC: 0.9801 Val PRC: 0.9798 Time: 0.71\n",
      "Epoch: 277 Train Loss: 0.1469 Acc: 0.9238 Pre: 0.9130 Recall: 0.9370 F1: 0.9248 Train AUC: 0.9853 Val AUC: 0.9792 Val PRC: 0.9788 Time: 0.73\n",
      "Epoch: 278 Train Loss: 0.1417 Acc: 0.9307 Pre: 0.9253 Recall: 0.9370 F1: 0.9311 Train AUC: 0.9862 Val AUC: 0.9800 Val PRC: 0.9799 Time: 0.69\n",
      "Epoch: 279 Train Loss: 0.1485 Acc: 0.9233 Pre: 0.9121 Recall: 0.9370 F1: 0.9244 Train AUC: 0.9846 Val AUC: 0.9797 Val PRC: 0.9799 Time: 0.72\n",
      "Epoch: 280 Train Loss: 0.1418 Acc: 0.9296 Pre: 0.9296 Recall: 0.9296 F1: 0.9296 Train AUC: 0.9857 Val AUC: 0.9796 Val PRC: 0.9777 Time: 0.72\n",
      "Epoch: 281 Train Loss: 0.1407 Acc: 0.9301 Pre: 0.9315 Recall: 0.9286 F1: 0.9300 Train AUC: 0.9865 Val AUC: 0.9822 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 282 Train Loss: 0.1366 Acc: 0.9275 Pre: 0.9046 Recall: 0.9559 F1: 0.9295 Train AUC: 0.9871 Val AUC: 0.9804 Val PRC: 0.9808 Time: 0.72\n",
      "Epoch: 283 Train Loss: 0.1408 Acc: 0.9328 Pre: 0.9310 Recall: 0.9349 F1: 0.9329 Train AUC: 0.9861 Val AUC: 0.9794 Val PRC: 0.9793 Time: 0.70\n",
      "Epoch: 284 Train Loss: 0.1380 Acc: 0.9286 Pre: 0.9163 Recall: 0.9433 F1: 0.9296 Train AUC: 0.9868 Val AUC: 0.9817 Val PRC: 0.9825 Time: 0.69\n",
      "Epoch: 285 Train Loss: 0.1461 Acc: 0.9270 Pre: 0.9135 Recall: 0.9433 F1: 0.9282 Train AUC: 0.9853 Val AUC: 0.9803 Val PRC: 0.9816 Time: 0.72\n",
      "Epoch: 286 Train Loss: 0.1375 Acc: 0.9223 Pre: 0.8964 Recall: 0.9548 F1: 0.9247 Train AUC: 0.9868 Val AUC: 0.9802 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 287 Train Loss: 0.1439 Acc: 0.9233 Pre: 0.9030 Recall: 0.9485 F1: 0.9252 Train AUC: 0.9878 Val AUC: 0.9803 Val PRC: 0.9803 Time: 0.70\n",
      "Epoch: 288 Train Loss: 0.1508 Acc: 0.9228 Pre: 0.9021 Recall: 0.9485 F1: 0.9247 Train AUC: 0.9846 Val AUC: 0.9801 Val PRC: 0.9814 Time: 0.74\n",
      "Epoch: 289 Train Loss: 0.1315 Acc: 0.9244 Pre: 0.9280 Recall: 0.9202 F1: 0.9241 Train AUC: 0.9882 Val AUC: 0.9812 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 290 Train Loss: 0.1421 Acc: 0.9291 Pre: 0.9190 Recall: 0.9412 F1: 0.9299 Train AUC: 0.9857 Val AUC: 0.9827 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 291 Train Loss: 0.1363 Acc: 0.9301 Pre: 0.9306 Recall: 0.9296 F1: 0.9301 Train AUC: 0.9870 Val AUC: 0.9830 Val PRC: 0.9829 Time: 0.72\n",
      "Epoch: 292 Train Loss: 0.1448 Acc: 0.9259 Pre: 0.9067 Recall: 0.9496 F1: 0.9277 Train AUC: 0.9860 Val AUC: 0.9817 Val PRC: 0.9816 Time: 0.71\n",
      "Epoch: 293 Train Loss: 0.1370 Acc: 0.9244 Pre: 0.9139 Recall: 0.9370 F1: 0.9253 Train AUC: 0.9867 Val AUC: 0.9788 Val PRC: 0.9763 Time: 0.90\n",
      "Epoch: 294 Train Loss: 0.1357 Acc: 0.9223 Pre: 0.9094 Recall: 0.9380 F1: 0.9235 Train AUC: 0.9872 Val AUC: 0.9787 Val PRC: 0.9781 Time: 0.72\n",
      "Epoch: 295 Train Loss: 0.1334 Acc: 0.9275 Pre: 0.9179 Recall: 0.9391 F1: 0.9283 Train AUC: 0.9879 Val AUC: 0.9811 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 296 Train Loss: 0.1286 Acc: 0.9249 Pre: 0.9271 Recall: 0.9223 F1: 0.9247 Train AUC: 0.9886 Val AUC: 0.9809 Val PRC: 0.9809 Time: 0.70\n",
      "Epoch: 297 Train Loss: 0.1362 Acc: 0.9307 Pre: 0.9227 Recall: 0.9401 F1: 0.9313 Train AUC: 0.9862 Val AUC: 0.9821 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 298 Train Loss: 0.1371 Acc: 0.9270 Pre: 0.9293 Recall: 0.9244 F1: 0.9268 Train AUC: 0.9867 Val AUC: 0.9811 Val PRC: 0.9818 Time: 0.71\n",
      "Epoch: 299 Train Loss: 0.1356 Acc: 0.9275 Pre: 0.9170 Recall: 0.9401 F1: 0.9284 Train AUC: 0.9868 Val AUC: 0.9818 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 300 Train Loss: 0.1369 Acc: 0.9291 Pre: 0.9260 Recall: 0.9328 F1: 0.9294 Train AUC: 0.9868 Val AUC: 0.9803 Val PRC: 0.9798 Time: 0.73\n",
      "Epoch: 301 Train Loss: 0.1357 Acc: 0.9307 Pre: 0.9125 Recall: 0.9527 F1: 0.9322 Train AUC: 0.9869 Val AUC: 0.9837 Val PRC: 0.9838 Time: 0.71\n",
      "Epoch: 302 Train Loss: 0.1349 Acc: 0.9286 Pre: 0.9295 Recall: 0.9275 F1: 0.9285 Train AUC: 0.9871 Val AUC: 0.9809 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 303 Train Loss: 0.1361 Acc: 0.9291 Pre: 0.9147 Recall: 0.9464 F1: 0.9303 Train AUC: 0.9864 Val AUC: 0.9804 Val PRC: 0.9788 Time: 0.72\n",
      "Epoch: 304 Train Loss: 0.1310 Acc: 0.9270 Pre: 0.9077 Recall: 0.9506 F1: 0.9287 Train AUC: 0.9881 Val AUC: 0.9824 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 305 Train Loss: 0.1347 Acc: 0.9254 Pre: 0.9167 Recall: 0.9359 F1: 0.9262 Train AUC: 0.9861 Val AUC: 0.9819 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 306 Train Loss: 0.1375 Acc: 0.9307 Pre: 0.9307 Recall: 0.9307 F1: 0.9307 Train AUC: 0.9865 Val AUC: 0.9809 Val PRC: 0.9812 Time: 0.75\n",
      "Epoch: 307 Train Loss: 0.1440 Acc: 0.9333 Pre: 0.9479 Recall: 0.9170 F1: 0.9322 Train AUC: 0.9853 Val AUC: 0.9830 Val PRC: 0.9841 Time: 0.71\n",
      "Epoch: 308 Train Loss: 0.1399 Acc: 0.9286 Pre: 0.9138 Recall: 0.9464 F1: 0.9298 Train AUC: 0.9863 Val AUC: 0.9808 Val PRC: 0.9816 Time: 0.71\n",
      "Epoch: 309 Train Loss: 0.1310 Acc: 0.9270 Pre: 0.9127 Recall: 0.9443 F1: 0.9282 Train AUC: 0.9877 Val AUC: 0.9812 Val PRC: 0.9821 Time: 0.73\n",
      "Epoch: 310 Train Loss: 0.1388 Acc: 0.9244 Pre: 0.9191 Recall: 0.9307 F1: 0.9248 Train AUC: 0.9862 Val AUC: 0.9810 Val PRC: 0.9823 Time: 0.72\n",
      "Epoch: 311 Train Loss: 0.1314 Acc: 0.9217 Pre: 0.8979 Recall: 0.9517 F1: 0.9240 Train AUC: 0.9876 Val AUC: 0.9803 Val PRC: 0.9818 Time: 0.70\n",
      "Epoch: 312 Train Loss: 0.1339 Acc: 0.9270 Pre: 0.9221 Recall: 0.9328 F1: 0.9274 Train AUC: 0.9864 Val AUC: 0.9823 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 313 Train Loss: 0.1323 Acc: 0.9312 Pre: 0.9151 Recall: 0.9506 F1: 0.9325 Train AUC: 0.9878 Val AUC: 0.9811 Val PRC: 0.9812 Time: 0.72\n",
      "Epoch: 314 Train Loss: 0.1372 Acc: 0.9249 Pre: 0.8939 Recall: 0.9643 F1: 0.9277 Train AUC: 0.9864 Val AUC: 0.9800 Val PRC: 0.9796 Time: 0.71\n",
      "Epoch: 315 Train Loss: 0.1272 Acc: 0.9333 Pre: 0.9283 Recall: 0.9391 F1: 0.9337 Train AUC: 0.9886 Val AUC: 0.9802 Val PRC: 0.9798 Time: 0.70\n",
      "Epoch: 316 Train Loss: 0.1249 Acc: 0.9270 Pre: 0.9110 Recall: 0.9464 F1: 0.9284 Train AUC: 0.9882 Val AUC: 0.9825 Val PRC: 0.9835 Time: 0.72\n",
      "Epoch: 317 Train Loss: 0.1295 Acc: 0.9244 Pre: 0.9056 Recall: 0.9475 F1: 0.9261 Train AUC: 0.9872 Val AUC: 0.9803 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 318 Train Loss: 0.1242 Acc: 0.9244 Pre: 0.9191 Recall: 0.9307 F1: 0.9248 Train AUC: 0.9888 Val AUC: 0.9814 Val PRC: 0.9819 Time: 0.70\n",
      "Epoch: 319 Train Loss: 0.1210 Acc: 0.9275 Pre: 0.8998 Recall: 0.9622 F1: 0.9299 Train AUC: 0.9893 Val AUC: 0.9798 Val PRC: 0.9795 Time: 0.70\n",
      "Epoch: 320 Train Loss: 0.1403 Acc: 0.9244 Pre: 0.9106 Recall: 0.9412 F1: 0.9256 Train AUC: 0.9854 Val AUC: 0.9797 Val PRC: 0.9807 Time: 0.69\n",
      "Epoch: 321 Train Loss: 0.1334 Acc: 0.9270 Pre: 0.9144 Recall: 0.9422 F1: 0.9281 Train AUC: 0.9874 Val AUC: 0.9814 Val PRC: 0.9823 Time: 0.71\n",
      "Epoch: 322 Train Loss: 0.1294 Acc: 0.9312 Pre: 0.9289 Recall: 0.9338 F1: 0.9314 Train AUC: 0.9882 Val AUC: 0.9804 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 323 Train Loss: 0.1226 Acc: 0.9312 Pre: 0.9142 Recall: 0.9517 F1: 0.9326 Train AUC: 0.9891 Val AUC: 0.9815 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 324 Train Loss: 0.1262 Acc: 0.9333 Pre: 0.9338 Recall: 0.9328 F1: 0.9333 Train AUC: 0.9883 Val AUC: 0.9806 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 325 Train Loss: 0.1268 Acc: 0.9249 Pre: 0.9157 Recall: 0.9359 F1: 0.9257 Train AUC: 0.9886 Val AUC: 0.9790 Val PRC: 0.9805 Time: 0.70\n",
      "Epoch: 326 Train Loss: 0.1312 Acc: 0.9196 Pre: 0.8944 Recall: 0.9517 F1: 0.9221 Train AUC: 0.9871 Val AUC: 0.9793 Val PRC: 0.9803 Time: 0.70\n",
      "Epoch: 327 Train Loss: 0.1345 Acc: 0.9244 Pre: 0.9114 Recall: 0.9401 F1: 0.9255 Train AUC: 0.9875 Val AUC: 0.9792 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 328 Train Loss: 0.1195 Acc: 0.9265 Pre: 0.9423 Recall: 0.9086 F1: 0.9251 Train AUC: 0.9900 Val AUC: 0.9808 Val PRC: 0.9816 Time: 0.72\n",
      "Epoch: 329 Train Loss: 0.1269 Acc: 0.9312 Pre: 0.9151 Recall: 0.9506 F1: 0.9325 Train AUC: 0.9882 Val AUC: 0.9799 Val PRC: 0.9787 Time: 0.71\n",
      "Epoch: 330 Train Loss: 0.1166 Acc: 0.9349 Pre: 0.9216 Recall: 0.9506 F1: 0.9359 Train AUC: 0.9899 Val AUC: 0.9824 Val PRC: 0.9819 Time: 0.73\n",
      "Epoch: 331 Train Loss: 0.1228 Acc: 0.9328 Pre: 0.9187 Recall: 0.9496 F1: 0.9339 Train AUC: 0.9892 Val AUC: 0.9837 Val PRC: 0.9843 Time: 0.70\n",
      "Epoch: 332 Train Loss: 0.1303 Acc: 0.9328 Pre: 0.9374 Recall: 0.9275 F1: 0.9324 Train AUC: 0.9874 Val AUC: 0.9822 Val PRC: 0.9832 Time: 0.69\n",
      "Epoch: 333 Train Loss: 0.1235 Acc: 0.9364 Pre: 0.9253 Recall: 0.9496 F1: 0.9373 Train AUC: 0.9885 Val AUC: 0.9828 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 334 Train Loss: 0.1277 Acc: 0.9322 Pre: 0.9458 Recall: 0.9170 F1: 0.9312 Train AUC: 0.9879 Val AUC: 0.9830 Val PRC: 0.9835 Time: 0.72\n",
      "Epoch: 335 Train Loss: 0.1120 Acc: 0.9312 Pre: 0.9298 Recall: 0.9328 F1: 0.9313 Train AUC: 0.9905 Val AUC: 0.9819 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 336 Train Loss: 0.1136 Acc: 0.9291 Pre: 0.9033 Recall: 0.9611 F1: 0.9313 Train AUC: 0.9906 Val AUC: 0.9832 Val PRC: 0.9834 Time: 0.73\n",
      "Epoch: 337 Train Loss: 0.1199 Acc: 0.9354 Pre: 0.9368 Recall: 0.9338 F1: 0.9353 Train AUC: 0.9897 Val AUC: 0.9840 Val PRC: 0.9845 Time: 0.73\n",
      "Epoch: 338 Train Loss: 0.1178 Acc: 0.9254 Pre: 0.9150 Recall: 0.9380 F1: 0.9263 Train AUC: 0.9898 Val AUC: 0.9811 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 339 Train Loss: 0.1181 Acc: 0.9291 Pre: 0.9190 Recall: 0.9412 F1: 0.9299 Train AUC: 0.9897 Val AUC: 0.9827 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 340 Train Loss: 0.1252 Acc: 0.9286 Pre: 0.9180 Recall: 0.9412 F1: 0.9295 Train AUC: 0.9879 Val AUC: 0.9809 Val PRC: 0.9802 Time: 0.73\n",
      "Epoch: 341 Train Loss: 0.1250 Acc: 0.9270 Pre: 0.9069 Recall: 0.9517 F1: 0.9288 Train AUC: 0.9876 Val AUC: 0.9820 Val PRC: 0.9824 Time: 0.72\n",
      "Epoch: 342 Train Loss: 0.1229 Acc: 0.9291 Pre: 0.9156 Recall: 0.9454 F1: 0.9302 Train AUC: 0.9898 Val AUC: 0.9813 Val PRC: 0.9817 Time: 0.70\n",
      "Epoch: 343 Train Loss: 0.1212 Acc: 0.9328 Pre: 0.9374 Recall: 0.9275 F1: 0.9324 Train AUC: 0.9883 Val AUC: 0.9816 Val PRC: 0.9821 Time: 0.73\n",
      "Epoch: 344 Train Loss: 0.1258 Acc: 0.9349 Pre: 0.9207 Recall: 0.9517 F1: 0.9360 Train AUC: 0.9883 Val AUC: 0.9829 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 345 Train Loss: 0.1245 Acc: 0.9343 Pre: 0.9207 Recall: 0.9506 F1: 0.9354 Train AUC: 0.9890 Val AUC: 0.9818 Val PRC: 0.9827 Time: 0.70\n",
      "Epoch: 346 Train Loss: 0.1246 Acc: 0.9338 Pre: 0.9357 Recall: 0.9317 F1: 0.9337 Train AUC: 0.9889 Val AUC: 0.9824 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 347 Train Loss: 0.1131 Acc: 0.9322 Pre: 0.9282 Recall: 0.9370 F1: 0.9326 Train AUC: 0.9908 Val AUC: 0.9817 Val PRC: 0.9825 Time: 0.70\n",
      "Epoch: 348 Train Loss: 0.1280 Acc: 0.9291 Pre: 0.9378 Recall: 0.9191 F1: 0.9284 Train AUC: 0.9887 Val AUC: 0.9822 Val PRC: 0.9826 Time: 0.70\n",
      "Epoch: 349 Train Loss: 0.1178 Acc: 0.9270 Pre: 0.9239 Recall: 0.9307 F1: 0.9273 Train AUC: 0.9904 Val AUC: 0.9806 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 350 Train Loss: 0.1171 Acc: 0.9286 Pre: 0.9155 Recall: 0.9443 F1: 0.9297 Train AUC: 0.9902 Val AUC: 0.9811 Val PRC: 0.9817 Time: 0.71\n",
      "Epoch: 351 Train Loss: 0.1275 Acc: 0.9354 Pre: 0.9359 Recall: 0.9349 F1: 0.9354 Train AUC: 0.9879 Val AUC: 0.9818 Val PRC: 0.9819 Time: 0.72\n",
      "Epoch: 352 Train Loss: 0.1187 Acc: 0.9328 Pre: 0.9346 Recall: 0.9307 F1: 0.9326 Train AUC: 0.9897 Val AUC: 0.9817 Val PRC: 0.9829 Time: 0.70\n",
      "Epoch: 353 Train Loss: 0.1127 Acc: 0.9259 Pre: 0.9176 Recall: 0.9359 F1: 0.9267 Train AUC: 0.9907 Val AUC: 0.9803 Val PRC: 0.9809 Time: 0.71\n",
      "Epoch: 354 Train Loss: 0.1115 Acc: 0.9291 Pre: 0.9332 Recall: 0.9244 F1: 0.9288 Train AUC: 0.9910 Val AUC: 0.9804 Val PRC: 0.9810 Time: 0.69\n",
      "Epoch: 355 Train Loss: 0.1196 Acc: 0.9301 Pre: 0.9288 Recall: 0.9317 F1: 0.9303 Train AUC: 0.9890 Val AUC: 0.9820 Val PRC: 0.9825 Time: 0.70\n",
      "Epoch: 356 Train Loss: 0.1161 Acc: 0.9307 Pre: 0.9253 Recall: 0.9370 F1: 0.9311 Train AUC: 0.9897 Val AUC: 0.9812 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 357 Train Loss: 0.1210 Acc: 0.9359 Pre: 0.9287 Recall: 0.9443 F1: 0.9365 Train AUC: 0.9913 Val AUC: 0.9832 Val PRC: 0.9832 Time: 0.69\n",
      "Epoch: 358 Train Loss: 0.1156 Acc: 0.9343 Pre: 0.9366 Recall: 0.9317 F1: 0.9342 Train AUC: 0.9896 Val AUC: 0.9824 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 359 Train Loss: 0.1127 Acc: 0.9349 Pre: 0.9148 Recall: 0.9590 F1: 0.9364 Train AUC: 0.9904 Val AUC: 0.9832 Val PRC: 0.9828 Time: 0.72\n",
      "Epoch: 360 Train Loss: 0.1149 Acc: 0.9386 Pre: 0.9291 Recall: 0.9496 F1: 0.9392 Train AUC: 0.9899 Val AUC: 0.9830 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 361 Train Loss: 0.1074 Acc: 0.9354 Pre: 0.9225 Recall: 0.9506 F1: 0.9364 Train AUC: 0.9920 Val AUC: 0.9830 Val PRC: 0.9839 Time: 0.71\n",
      "Epoch: 362 Train Loss: 0.1225 Acc: 0.9375 Pre: 0.9272 Recall: 0.9496 F1: 0.9382 Train AUC: 0.9910 Val AUC: 0.9832 Val PRC: 0.9839 Time: 0.71\n",
      "Epoch: 363 Train Loss: 0.1389 Acc: 0.9364 Pre: 0.9378 Recall: 0.9349 F1: 0.9363 Train AUC: 0.9872 Val AUC: 0.9828 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 364 Train Loss: 0.1165 Acc: 0.9364 Pre: 0.9360 Recall: 0.9370 F1: 0.9365 Train AUC: 0.9897 Val AUC: 0.9828 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 365 Train Loss: 0.1169 Acc: 0.9249 Pre: 0.9200 Recall: 0.9307 F1: 0.9253 Train AUC: 0.9907 Val AUC: 0.9806 Val PRC: 0.9814 Time: 0.76\n",
      "Epoch: 366 Train Loss: 0.1148 Acc: 0.9322 Pre: 0.9411 Recall: 0.9223 F1: 0.9316 Train AUC: 0.9897 Val AUC: 0.9823 Val PRC: 0.9827 Time: 0.73\n",
      "Epoch: 367 Train Loss: 0.1165 Acc: 0.9322 Pre: 0.9336 Recall: 0.9307 F1: 0.9321 Train AUC: 0.9905 Val AUC: 0.9830 Val PRC: 0.9835 Time: 0.72\n",
      "Epoch: 368 Train Loss: 0.1123 Acc: 0.9328 Pre: 0.9374 Recall: 0.9275 F1: 0.9324 Train AUC: 0.9907 Val AUC: 0.9822 Val PRC: 0.9830 Time: 0.74\n",
      "Epoch: 369 Train Loss: 0.1150 Acc: 0.9286 Pre: 0.9277 Recall: 0.9296 F1: 0.9286 Train AUC: 0.9904 Val AUC: 0.9811 Val PRC: 0.9816 Time: 0.74\n",
      "Epoch: 370 Train Loss: 0.1066 Acc: 0.9322 Pre: 0.9345 Recall: 0.9296 F1: 0.9321 Train AUC: 0.9918 Val AUC: 0.9805 Val PRC: 0.9810 Time: 0.73\n",
      "Epoch: 371 Train Loss: 0.1188 Acc: 0.9375 Pre: 0.9407 Recall: 0.9338 F1: 0.9373 Train AUC: 0.9893 Val AUC: 0.9832 Val PRC: 0.9835 Time: 0.73\n",
      "Epoch: 372 Train Loss: 0.1097 Acc: 0.9322 Pre: 0.9345 Recall: 0.9296 F1: 0.9321 Train AUC: 0.9905 Val AUC: 0.9822 Val PRC: 0.9823 Time: 0.72\n",
      "Epoch: 373 Train Loss: 0.1145 Acc: 0.9301 Pre: 0.9261 Recall: 0.9349 F1: 0.9305 Train AUC: 0.9908 Val AUC: 0.9828 Val PRC: 0.9829 Time: 0.70\n",
      "Epoch: 374 Train Loss: 0.1068 Acc: 0.9328 Pre: 0.9187 Recall: 0.9496 F1: 0.9339 Train AUC: 0.9908 Val AUC: 0.9816 Val PRC: 0.9805 Time: 0.70\n",
      "Epoch: 375 Train Loss: 0.1191 Acc: 0.9265 Pre: 0.9212 Recall: 0.9328 F1: 0.9269 Train AUC: 0.9901 Val AUC: 0.9803 Val PRC: 0.9802 Time: 0.69\n",
      "Epoch: 376 Train Loss: 0.1115 Acc: 0.9270 Pre: 0.9119 Recall: 0.9454 F1: 0.9283 Train AUC: 0.9902 Val AUC: 0.9816 Val PRC: 0.9824 Time: 0.70\n",
      "Epoch: 377 Train Loss: 0.1209 Acc: 0.9312 Pre: 0.9185 Recall: 0.9464 F1: 0.9322 Train AUC: 0.9893 Val AUC: 0.9814 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 378 Train Loss: 0.1164 Acc: 0.9301 Pre: 0.9279 Recall: 0.9328 F1: 0.9303 Train AUC: 0.9901 Val AUC: 0.9816 Val PRC: 0.9823 Time: 0.70\n",
      "Epoch: 379 Train Loss: 0.1057 Acc: 0.9301 Pre: 0.9315 Recall: 0.9286 F1: 0.9300 Train AUC: 0.9917 Val AUC: 0.9833 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 380 Train Loss: 0.1180 Acc: 0.9370 Pre: 0.9333 Recall: 0.9412 F1: 0.9372 Train AUC: 0.9920 Val AUC: 0.9842 Val PRC: 0.9848 Time: 0.71\n",
      "Epoch: 381 Train Loss: 0.1125 Acc: 0.9275 Pre: 0.9339 Recall: 0.9202 F1: 0.9270 Train AUC: 0.9903 Val AUC: 0.9813 Val PRC: 0.9817 Time: 0.71\n",
      "Epoch: 382 Train Loss: 0.1104 Acc: 0.9265 Pre: 0.9060 Recall: 0.9517 F1: 0.9283 Train AUC: 0.9916 Val AUC: 0.9823 Val PRC: 0.9828 Time: 0.70\n",
      "Epoch: 383 Train Loss: 0.1121 Acc: 0.9338 Pre: 0.9249 Recall: 0.9443 F1: 0.9345 Train AUC: 0.9902 Val AUC: 0.9840 Val PRC: 0.9846 Time: 0.70\n",
      "Epoch: 384 Train Loss: 0.1006 Acc: 0.9312 Pre: 0.9326 Recall: 0.9296 F1: 0.9311 Train AUC: 0.9924 Val AUC: 0.9830 Val PRC: 0.9840 Time: 0.70\n",
      "Epoch: 385 Train Loss: 0.1062 Acc: 0.9317 Pre: 0.9177 Recall: 0.9485 F1: 0.9329 Train AUC: 0.9913 Val AUC: 0.9836 Val PRC: 0.9844 Time: 0.71\n",
      "Epoch: 386 Train Loss: 0.1124 Acc: 0.9380 Pre: 0.9446 Recall: 0.9307 F1: 0.9376 Train AUC: 0.9894 Val AUC: 0.9839 Val PRC: 0.9846 Time: 0.72\n",
      "Epoch: 387 Train Loss: 0.1077 Acc: 0.9370 Pre: 0.9454 Recall: 0.9275 F1: 0.9364 Train AUC: 0.9908 Val AUC: 0.9832 Val PRC: 0.9839 Time: 0.72\n",
      "Epoch: 388 Train Loss: 0.1124 Acc: 0.9280 Pre: 0.9171 Recall: 0.9412 F1: 0.9290 Train AUC: 0.9900 Val AUC: 0.9833 Val PRC: 0.9843 Time: 0.69\n",
      "Epoch: 389 Train Loss: 0.1136 Acc: 0.9380 Pre: 0.9380 Recall: 0.9380 F1: 0.9380 Train AUC: 0.9893 Val AUC: 0.9833 Val PRC: 0.9838 Time: 0.72\n",
      "Epoch: 390 Train Loss: 0.0989 Acc: 0.9333 Pre: 0.9328 Recall: 0.9338 F1: 0.9333 Train AUC: 0.9924 Val AUC: 0.9831 Val PRC: 0.9841 Time: 0.72\n",
      "Epoch: 391 Train Loss: 0.1184 Acc: 0.9333 Pre: 0.9310 Recall: 0.9359 F1: 0.9335 Train AUC: 0.9895 Val AUC: 0.9836 Val PRC: 0.9844 Time: 0.70\n",
      "Epoch: 392 Train Loss: 0.0923 Acc: 0.9354 Pre: 0.9295 Recall: 0.9422 F1: 0.9358 Train AUC: 0.9933 Val AUC: 0.9849 Val PRC: 0.9860 Time: 0.71\n",
      "Epoch: 393 Train Loss: 0.1049 Acc: 0.9349 Pre: 0.9304 Recall: 0.9401 F1: 0.9352 Train AUC: 0.9911 Val AUC: 0.9826 Val PRC: 0.9830 Time: 0.73\n",
      "Epoch: 394 Train Loss: 0.1079 Acc: 0.9312 Pre: 0.9134 Recall: 0.9527 F1: 0.9326 Train AUC: 0.9909 Val AUC: 0.9829 Val PRC: 0.9833 Time: 0.70\n",
      "Epoch: 395 Train Loss: 0.1068 Acc: 0.9359 Pre: 0.9217 Recall: 0.9527 F1: 0.9370 Train AUC: 0.9903 Val AUC: 0.9830 Val PRC: 0.9831 Time: 0.73\n",
      "Epoch: 396 Train Loss: 0.1019 Acc: 0.9301 Pre: 0.9191 Recall: 0.9433 F1: 0.9311 Train AUC: 0.9914 Val AUC: 0.9804 Val PRC: 0.9776 Time: 0.74\n",
      "Epoch: 397 Train Loss: 0.1117 Acc: 0.9328 Pre: 0.9265 Recall: 0.9401 F1: 0.9333 Train AUC: 0.9907 Val AUC: 0.9815 Val PRC: 0.9821 Time: 0.69\n",
      "Epoch: 398 Train Loss: 0.1205 Acc: 0.9349 Pre: 0.9207 Recall: 0.9517 F1: 0.9360 Train AUC: 0.9906 Val AUC: 0.9819 Val PRC: 0.9821 Time: 0.70\n",
      "Epoch: 399 Train Loss: 0.1024 Acc: 0.9338 Pre: 0.9275 Recall: 0.9412 F1: 0.9343 Train AUC: 0.9909 Val AUC: 0.9799 Val PRC: 0.9766 Time: 0.72\n",
      "Epoch: 400 Train Loss: 0.1048 Acc: 0.9354 Pre: 0.9251 Recall: 0.9475 F1: 0.9362 Train AUC: 0.9912 Val AUC: 0.9823 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 401 Train Loss: 0.1223 Acc: 0.9317 Pre: 0.9281 Recall: 0.9359 F1: 0.9320 Train AUC: 0.9881 Val AUC: 0.9798 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 402 Train Loss: 0.1037 Acc: 0.9391 Pre: 0.9292 Recall: 0.9506 F1: 0.9398 Train AUC: 0.9910 Val AUC: 0.9821 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 403 Train Loss: 0.1014 Acc: 0.9328 Pre: 0.9112 Recall: 0.9590 F1: 0.9345 Train AUC: 0.9915 Val AUC: 0.9822 Val PRC: 0.9835 Time: 0.69\n",
      "Epoch: 404 Train Loss: 0.1069 Acc: 0.9322 Pre: 0.9238 Recall: 0.9422 F1: 0.9329 Train AUC: 0.9905 Val AUC: 0.9818 Val PRC: 0.9818 Time: 0.70\n",
      "Epoch: 405 Train Loss: 0.1030 Acc: 0.9349 Pre: 0.9304 Recall: 0.9401 F1: 0.9352 Train AUC: 0.9915 Val AUC: 0.9821 Val PRC: 0.9832 Time: 0.70\n",
      "Epoch: 406 Train Loss: 0.1124 Acc: 0.9354 Pre: 0.9349 Recall: 0.9359 F1: 0.9354 Train AUC: 0.9897 Val AUC: 0.9822 Val PRC: 0.9825 Time: 0.70\n",
      "Epoch: 407 Train Loss: 0.1014 Acc: 0.9380 Pre: 0.9353 Recall: 0.9412 F1: 0.9458 Train AUC: 0.9916 Val AUC: 0.9840 Val PRC: 0.9852 Time: 0.73\n",
      "Epoch: 408 Train Loss: 0.1072 Acc: 0.9317 Pre: 0.9246 Recall: 0.9401 F1: 0.9323 Train AUC: 0.9904 Val AUC: 0.9810 Val PRC: 0.9806 Time: 0.72\n",
      "Epoch: 409 Train Loss: 0.1066 Acc: 0.9333 Pre: 0.9257 Recall: 0.9422 F1: 0.9339 Train AUC: 0.9909 Val AUC: 0.9830 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 410 Train Loss: 0.1040 Acc: 0.9275 Pre: 0.9086 Recall: 0.9506 F1: 0.9292 Train AUC: 0.9913 Val AUC: 0.9813 Val PRC: 0.9814 Time: 0.70\n",
      "Epoch: 411 Train Loss: 0.1062 Acc: 0.9291 Pre: 0.9332 Recall: 0.9244 F1: 0.9288 Train AUC: 0.9915 Val AUC: 0.9818 Val PRC: 0.9824 Time: 0.70\n",
      "Epoch: 412 Train Loss: 0.1040 Acc: 0.9370 Pre: 0.9483 Recall: 0.9244 F1: 0.9362 Train AUC: 0.9911 Val AUC: 0.9830 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 413 Train Loss: 0.1017 Acc: 0.9343 Pre: 0.9232 Recall: 0.9475 F1: 0.9352 Train AUC: 0.9914 Val AUC: 0.9830 Val PRC: 0.9835 Time: 0.70\n",
      "Epoch: 414 Train Loss: 0.1009 Acc: 0.9286 Pre: 0.9206 Recall: 0.9380 F1: 0.9292 Train AUC: 0.9917 Val AUC: 0.9813 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 415 Train Loss: 0.1118 Acc: 0.9370 Pre: 0.9464 Recall: 0.9265 F1: 0.9363 Train AUC: 0.9894 Val AUC: 0.9832 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 416 Train Loss: 0.1051 Acc: 0.9359 Pre: 0.9278 Recall: 0.9454 F1: 0.9365 Train AUC: 0.9909 Val AUC: 0.9827 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 417 Train Loss: 0.0974 Acc: 0.9407 Pre: 0.9294 Recall: 0.9538 F1: 0.9414 Train AUC: 0.9920 Val AUC: 0.9840 Val PRC: 0.9844 Time: 0.71\n",
      "Epoch: 418 Train Loss: 0.0970 Acc: 0.9375 Pre: 0.9380 Recall: 0.9370 F1: 0.9375 Train AUC: 0.9922 Val AUC: 0.9827 Val PRC: 0.9827 Time: 0.71\n",
      "Epoch: 419 Train Loss: 0.0986 Acc: 0.9422 Pre: 0.9450 Recall: 0.9391 F1: 0.9420 Train AUC: 0.9912 Val AUC: 0.9820 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 420 Train Loss: 0.1091 Acc: 0.9375 Pre: 0.9325 Recall: 0.9433 F1: 0.9379 Train AUC: 0.9901 Val AUC: 0.9839 Val PRC: 0.9849 Time: 0.71\n",
      "Epoch: 421 Train Loss: 0.1032 Acc: 0.9354 Pre: 0.9313 Recall: 0.9401 F1: 0.9357 Train AUC: 0.9914 Val AUC: 0.9823 Val PRC: 0.9827 Time: 0.75\n",
      "Epoch: 422 Train Loss: 0.1120 Acc: 0.9364 Pre: 0.9324 Recall: 0.9412 F1: 0.9367 Train AUC: 0.9915 Val AUC: 0.9835 Val PRC: 0.9846 Time: 0.70\n",
      "Epoch: 423 Train Loss: 0.0995 Acc: 0.9354 Pre: 0.9200 Recall: 0.9538 F1: 0.9366 Train AUC: 0.9921 Val AUC: 0.9839 Val PRC: 0.9845 Time: 0.71\n",
      "Epoch: 424 Train Loss: 0.1158 Acc: 0.9370 Pre: 0.9236 Recall: 0.9527 F1: 0.9380 Train AUC: 0.9889 Val AUC: 0.9828 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 425 Train Loss: 0.1138 Acc: 0.9338 Pre: 0.9347 Recall: 0.9328 F1: 0.9338 Train AUC: 0.9906 Val AUC: 0.9826 Val PRC: 0.9835 Time: 0.74\n",
      "Epoch: 426 Train Loss: 0.0955 Acc: 0.9380 Pre: 0.9317 Recall: 0.9454 F1: 0.9385 Train AUC: 0.9927 Val AUC: 0.9838 Val PRC: 0.9848 Time: 0.94\n",
      "Epoch: 427 Train Loss: 0.0988 Acc: 0.9386 Pre: 0.9381 Recall: 0.9391 F1: 0.9386 Train AUC: 0.9933 Val AUC: 0.9835 Val PRC: 0.9844 Time: 0.72\n",
      "Epoch: 428 Train Loss: 0.0969 Acc: 0.9338 Pre: 0.9163 Recall: 0.9548 F1: 0.9352 Train AUC: 0.9923 Val AUC: 0.9831 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 429 Train Loss: 0.0984 Acc: 0.9359 Pre: 0.9387 Recall: 0.9328 F1: 0.9357 Train AUC: 0.9919 Val AUC: 0.9830 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 430 Train Loss: 0.1022 Acc: 0.9391 Pre: 0.9363 Recall: 0.9422 F1: 0.9393 Train AUC: 0.9919 Val AUC: 0.9834 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 431 Train Loss: 0.1070 Acc: 0.9354 Pre: 0.9304 Recall: 0.9412 F1: 0.9358 Train AUC: 0.9907 Val AUC: 0.9821 Val PRC: 0.9821 Time: 0.71\n",
      "Epoch: 432 Train Loss: 0.0984 Acc: 0.9333 Pre: 0.9384 Recall: 0.9275 F1: 0.9329 Train AUC: 0.9919 Val AUC: 0.9819 Val PRC: 0.9829 Time: 0.72\n",
      "Epoch: 433 Train Loss: 0.0918 Acc: 0.9322 Pre: 0.9354 Recall: 0.9286 F1: 0.9320 Train AUC: 0.9931 Val AUC: 0.9829 Val PRC: 0.9835 Time: 0.72\n",
      "Epoch: 434 Train Loss: 0.0896 Acc: 0.9328 Pre: 0.9196 Recall: 0.9485 F1: 0.9338 Train AUC: 0.9932 Val AUC: 0.9820 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 435 Train Loss: 0.0994 Acc: 0.9338 Pre: 0.9249 Recall: 0.9443 F1: 0.9345 Train AUC: 0.9916 Val AUC: 0.9836 Val PRC: 0.9845 Time: 0.71\n",
      "Epoch: 436 Train Loss: 0.0896 Acc: 0.9364 Pre: 0.9351 Recall: 0.9380 F1: 0.9365 Train AUC: 0.9928 Val AUC: 0.9840 Val PRC: 0.9846 Time: 0.72\n",
      "Epoch: 437 Train Loss: 0.1014 Acc: 0.9349 Pre: 0.9268 Recall: 0.9443 F1: 0.9355 Train AUC: 0.9911 Val AUC: 0.9835 Val PRC: 0.9839 Time: 0.71\n",
      "Epoch: 438 Train Loss: 0.0877 Acc: 0.9328 Pre: 0.9578 Recall: 0.9055 F1: 0.9309 Train AUC: 0.9932 Val AUC: 0.9830 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 439 Train Loss: 0.0979 Acc: 0.9307 Pre: 0.9150 Recall: 0.9496 F1: 0.9320 Train AUC: 0.9907 Val AUC: 0.9809 Val PRC: 0.9806 Time: 0.72\n",
      "Epoch: 440 Train Loss: 0.0981 Acc: 0.9359 Pre: 0.9443 Recall: 0.9265 F1: 0.9353 Train AUC: 0.9917 Val AUC: 0.9833 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 441 Train Loss: 0.0954 Acc: 0.9359 Pre: 0.9387 Recall: 0.9328 F1: 0.9357 Train AUC: 0.9919 Val AUC: 0.9846 Val PRC: 0.9857 Time: 0.70\n",
      "Epoch: 442 Train Loss: 0.0966 Acc: 0.9396 Pre: 0.9534 Recall: 0.9244 F1: 0.9387 Train AUC: 0.9920 Val AUC: 0.9826 Val PRC: 0.9839 Time: 0.72\n",
      "Epoch: 443 Train Loss: 0.0967 Acc: 0.9407 Pre: 0.9374 Recall: 0.9443 F1: 0.9409 Train AUC: 0.9914 Val AUC: 0.9825 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 444 Train Loss: 0.0909 Acc: 0.9349 Pre: 0.9471 Recall: 0.9212 F1: 0.9340 Train AUC: 0.9928 Val AUC: 0.9824 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 445 Train Loss: 0.0958 Acc: 0.9380 Pre: 0.9417 Recall: 0.9338 F1: 0.9378 Train AUC: 0.9918 Val AUC: 0.9831 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 446 Train Loss: 0.0902 Acc: 0.9333 Pre: 0.9479 Recall: 0.9170 F1: 0.9322 Train AUC: 0.9933 Val AUC: 0.9821 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 447 Train Loss: 0.0964 Acc: 0.9296 Pre: 0.9446 Recall: 0.9128 F1: 0.9284 Train AUC: 0.9925 Val AUC: 0.9819 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 448 Train Loss: 0.0903 Acc: 0.9349 Pre: 0.9395 Recall: 0.9296 F1: 0.9345 Train AUC: 0.9931 Val AUC: 0.9835 Val PRC: 0.9849 Time: 0.74\n",
      "Epoch: 449 Train Loss: 0.0815 Acc: 0.9391 Pre: 0.9345 Recall: 0.9443 F1: 0.9394 Train AUC: 0.9948 Val AUC: 0.9815 Val PRC: 0.9828 Time: 0.72\n",
      "Epoch: 450 Train Loss: 0.0867 Acc: 0.9359 Pre: 0.9415 Recall: 0.9296 F1: 0.9355 Train AUC: 0.9938 Val AUC: 0.9840 Val PRC: 0.9851 Time: 0.71\n",
      "Epoch: 451 Train Loss: 0.0986 Acc: 0.9333 Pre: 0.9412 Recall: 0.9244 F1: 0.9327 Train AUC: 0.9916 Val AUC: 0.9829 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 452 Train Loss: 0.0938 Acc: 0.9354 Pre: 0.9520 Recall: 0.9170 F1: 0.9342 Train AUC: 0.9925 Val AUC: 0.9837 Val PRC: 0.9842 Time: 0.72\n",
      "Epoch: 453 Train Loss: 0.1042 Acc: 0.9359 Pre: 0.9359 Recall: 0.9359 F1: 0.9359 Train AUC: 0.9912 Val AUC: 0.9830 Val PRC: 0.9827 Time: 0.70\n",
      "Epoch: 454 Train Loss: 0.0852 Acc: 0.9343 Pre: 0.9509 Recall: 0.9160 F1: 0.9331 Train AUC: 0.9938 Val AUC: 0.9836 Val PRC: 0.9841 Time: 0.75\n",
      "Epoch: 455 Train Loss: 0.0890 Acc: 0.9328 Pre: 0.9196 Recall: 0.9485 F1: 0.9338 Train AUC: 0.9933 Val AUC: 0.9829 Val PRC: 0.9833 Time: 0.72\n",
      "Epoch: 456 Train Loss: 0.0890 Acc: 0.9343 Pre: 0.9294 Recall: 0.9401 F1: 0.9347 Train AUC: 0.9930 Val AUC: 0.9830 Val PRC: 0.9832 Time: 0.71\n",
      "Epoch: 457 Train Loss: 0.1078 Acc: 0.9359 Pre: 0.9296 Recall: 0.9433 F1: 0.9364 Train AUC: 0.9910 Val AUC: 0.9838 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 458 Train Loss: 0.0910 Acc: 0.9359 Pre: 0.9359 Recall: 0.9359 F1: 0.9359 Train AUC: 0.9927 Val AUC: 0.9851 Val PRC: 0.9856 Time: 0.72\n",
      "Epoch: 459 Train Loss: 0.0979 Acc: 0.9328 Pre: 0.9346 Recall: 0.9307 F1: 0.9326 Train AUC: 0.9909 Val AUC: 0.9828 Val PRC: 0.9830 Time: 0.70\n",
      "Epoch: 460 Train Loss: 0.0832 Acc: 0.9375 Pre: 0.9417 Recall: 0.9328 F1: 0.9372 Train AUC: 0.9942 Val AUC: 0.9844 Val PRC: 0.9850 Time: 0.71\n",
      "Epoch: 461 Train Loss: 0.0987 Acc: 0.9359 Pre: 0.9540 Recall: 0.9160 F1: 0.9346 Train AUC: 0.9915 Val AUC: 0.9840 Val PRC: 0.9848 Time: 0.70\n",
      "Epoch: 462 Train Loss: 0.0869 Acc: 0.9386 Pre: 0.9372 Recall: 0.9401 F1: 0.9386 Train AUC: 0.9934 Val AUC: 0.9848 Val PRC: 0.9853 Time: 0.72\n",
      "Epoch: 463 Train Loss: 0.0848 Acc: 0.9380 Pre: 0.9299 Recall: 0.9475 F1: 0.9386 Train AUC: 0.9940 Val AUC: 0.9836 Val PRC: 0.9844 Time: 0.73\n",
      "Epoch: 464 Train Loss: 0.0968 Acc: 0.9396 Pre: 0.9364 Recall: 0.9433 F1: 0.9398 Train AUC: 0.9922 Val AUC: 0.9831 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 465 Train Loss: 0.0909 Acc: 0.9359 Pre: 0.9368 Recall: 0.9349 F1: 0.9359 Train AUC: 0.9926 Val AUC: 0.9827 Val PRC: 0.9833 Time: 0.70\n",
      "Epoch: 466 Train Loss: 0.0814 Acc: 0.9359 Pre: 0.9368 Recall: 0.9349 F1: 0.9359 Train AUC: 0.9944 Val AUC: 0.9829 Val PRC: 0.9841 Time: 0.71\n",
      "Epoch: 467 Train Loss: 0.0877 Acc: 0.9296 Pre: 0.9323 Recall: 0.9265 F1: 0.9294 Train AUC: 0.9926 Val AUC: 0.9806 Val PRC: 0.9819 Time: 0.70\n",
      "Epoch: 468 Train Loss: 0.0892 Acc: 0.9333 Pre: 0.9266 Recall: 0.9412 F1: 0.9338 Train AUC: 0.9928 Val AUC: 0.9819 Val PRC: 0.9831 Time: 0.71\n",
      "Epoch: 469 Train Loss: 0.0982 Acc: 0.9333 Pre: 0.9113 Recall: 0.9601 F1: 0.9350 Train AUC: 0.9938 Val AUC: 0.9823 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 470 Train Loss: 0.0839 Acc: 0.9380 Pre: 0.9562 Recall: 0.9181 F1: 0.9368 Train AUC: 0.9941 Val AUC: 0.9826 Val PRC: 0.9840 Time: 0.73\n",
      "Epoch: 471 Train Loss: 0.0988 Acc: 0.9343 Pre: 0.9330 Recall: 0.9359 F1: 0.9345 Train AUC: 0.9910 Val AUC: 0.9823 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 472 Train Loss: 0.0779 Acc: 0.9370 Pre: 0.9361 Recall: 0.9380 F1: 0.9370 Train AUC: 0.9939 Val AUC: 0.9815 Val PRC: 0.9817 Time: 0.70\n",
      "Epoch: 473 Train Loss: 0.0919 Acc: 0.9364 Pre: 0.9369 Recall: 0.9359 F1: 0.9364 Train AUC: 0.9918 Val AUC: 0.9831 Val PRC: 0.9843 Time: 0.72\n",
      "Epoch: 474 Train Loss: 0.0890 Acc: 0.9317 Pre: 0.9237 Recall: 0.9412 F1: 0.9324 Train AUC: 0.9928 Val AUC: 0.9822 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 475 Train Loss: 0.0896 Acc: 0.9359 Pre: 0.9350 Recall: 0.9370 F1: 0.9360 Train AUC: 0.9924 Val AUC: 0.9810 Val PRC: 0.9821 Time: 0.70\n",
      "Epoch: 476 Train Loss: 0.0791 Acc: 0.9391 Pre: 0.9382 Recall: 0.9401 F1: 0.9391 Train AUC: 0.9947 Val AUC: 0.9822 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 477 Train Loss: 0.0809 Acc: 0.9343 Pre: 0.9207 Recall: 0.9506 F1: 0.9354 Train AUC: 0.9945 Val AUC: 0.9825 Val PRC: 0.9836 Time: 0.72\n",
      "Epoch: 478 Train Loss: 0.0918 Acc: 0.9401 Pre: 0.9476 Recall: 0.9317 F1: 0.9396 Train AUC: 0.9930 Val AUC: 0.9829 Val PRC: 0.9838 Time: 0.73\n",
      "Epoch: 479 Train Loss: 0.0846 Acc: 0.9338 Pre: 0.9422 Recall: 0.9244 F1: 0.9332 Train AUC: 0.9932 Val AUC: 0.9807 Val PRC: 0.9824 Time: 0.73\n",
      "Epoch: 480 Train Loss: 0.0840 Acc: 0.9364 Pre: 0.9360 Recall: 0.9370 F1: 0.9365 Train AUC: 0.9938 Val AUC: 0.9811 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 481 Train Loss: 0.0801 Acc: 0.9328 Pre: 0.9355 Recall: 0.9296 F1: 0.9326 Train AUC: 0.9943 Val AUC: 0.9808 Val PRC: 0.9826 Time: 0.70\n",
      "Epoch: 482 Train Loss: 0.1233 Acc: 0.9333 Pre: 0.9257 Recall: 0.9422 F1: 0.9339 Train AUC: 0.9906 Val AUC: 0.9822 Val PRC: 0.9837 Time: 0.74\n",
      "Epoch: 483 Train Loss: 0.0832 Acc: 0.9396 Pre: 0.9486 Recall: 0.9296 F1: 0.9390 Train AUC: 0.9934 Val AUC: 0.9828 Val PRC: 0.9843 Time: 0.71\n",
      "Epoch: 484 Train Loss: 0.0878 Acc: 0.9359 Pre: 0.9368 Recall: 0.9349 F1: 0.9359 Train AUC: 0.9925 Val AUC: 0.9827 Val PRC: 0.9832 Time: 0.75\n",
      "Epoch: 485 Train Loss: 0.0813 Acc: 0.9386 Pre: 0.9494 Recall: 0.9265 F1: 0.9378 Train AUC: 0.9935 Val AUC: 0.9837 Val PRC: 0.9850 Time: 0.72\n",
      "Epoch: 486 Train Loss: 0.0917 Acc: 0.9370 Pre: 0.9298 Recall: 0.9454 F1: 0.9375 Train AUC: 0.9922 Val AUC: 0.9837 Val PRC: 0.9847 Time: 0.74\n",
      "Epoch: 487 Train Loss: 0.0911 Acc: 0.9322 Pre: 0.9238 Recall: 0.9422 F1: 0.9329 Train AUC: 0.9915 Val AUC: 0.9816 Val PRC: 0.9820 Time: 0.71\n",
      "Epoch: 488 Train Loss: 0.1023 Acc: 0.9417 Pre: 0.9440 Recall: 0.9391 F1: 0.9415 Train AUC: 0.9923 Val AUC: 0.9821 Val PRC: 0.9819 Time: 0.75\n",
      "Epoch: 489 Train Loss: 0.0816 Acc: 0.9375 Pre: 0.9464 Recall: 0.9275 F1: 0.9369 Train AUC: 0.9936 Val AUC: 0.9830 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 490 Train Loss: 0.0763 Acc: 0.9391 Pre: 0.9504 Recall: 0.9265 F1: 0.9383 Train AUC: 0.9949 Val AUC: 0.9820 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 491 Train Loss: 0.0890 Acc: 0.9401 Pre: 0.9374 Recall: 0.9433 F1: 0.9403 Train AUC: 0.9929 Val AUC: 0.9822 Val PRC: 0.9836 Time: 0.73\n",
      "Epoch: 492 Train Loss: 0.0819 Acc: 0.9343 Pre: 0.9303 Recall: 0.9391 F1: 0.9347 Train AUC: 0.9933 Val AUC: 0.9817 Val PRC: 0.9831 Time: 0.70\n",
      "Epoch: 493 Train Loss: 0.0873 Acc: 0.9333 Pre: 0.9356 Recall: 0.9307 F1: 0.9331 Train AUC: 0.9929 Val AUC: 0.9802 Val PRC: 0.9802 Time: 0.71\n",
      "Epoch: 494 Train Loss: 0.0940 Acc: 0.9370 Pre: 0.9407 Recall: 0.9328 F1: 0.9367 Train AUC: 0.9919 Val AUC: 0.9801 Val PRC: 0.9813 Time: 0.72\n",
      "Epoch: 495 Train Loss: 0.0995 Acc: 0.9386 Pre: 0.9475 Recall: 0.9286 F1: 0.9379 Train AUC: 0.9916 Val AUC: 0.9802 Val PRC: 0.9796 Time: 0.71\n",
      "Epoch: 496 Train Loss: 0.0788 Acc: 0.9338 Pre: 0.9394 Recall: 0.9275 F1: 0.9334 Train AUC: 0.9954 Val AUC: 0.9813 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 497 Train Loss: 0.0880 Acc: 0.9317 Pre: 0.9317 Recall: 0.9317 F1: 0.9317 Train AUC: 0.9929 Val AUC: 0.9805 Val PRC: 0.9819 Time: 0.73\n",
      "Epoch: 498 Train Loss: 0.0896 Acc: 0.9354 Pre: 0.9368 Recall: 0.9338 F1: 0.9353 Train AUC: 0.9932 Val AUC: 0.9828 Val PRC: 0.9843 Time: 0.73\n",
      "Epoch: 499 Train Loss: 0.0784 Acc: 0.9349 Pre: 0.9423 Recall: 0.9265 F1: 0.9343 Train AUC: 0.9940 Val AUC: 0.9820 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 500 Train Loss: 0.0823 Acc: 0.9386 Pre: 0.9399 Recall: 0.9370 F1: 0.9385 Train AUC: 0.9941 Val AUC: 0.9835 Val PRC: 0.9848 Time: 0.72\n",
      "Fold: 2 Best Epoch: 407 Val acc: 0.9380 Val Pre: 0.9353 Val Recall: 0.9412 Val F1: 0.9382 Val AUC: 0.9840 Val PRC: 0.9852\n",
      "------this is 3th cross validation------\n",
      "total params: 307522\n"
     ]
    },
    
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.6917 Acc: 0.5200 Pre: 0.5103 Recall: 0.9842 F1: 0.6722 Train AUC: 0.5172 Val AUC: 0.5130 Val PRC: 0.5141 Time: 0.79\n",
      "Epoch: 2 Train Loss: 0.6982 Acc: 0.5026 Pre: 0.5013 Recall: 0.9979 F1: 0.6674 Train AUC: 0.5321 Val AUC: 0.5194 Val PRC: 0.5261 Time: 0.77\n",
      "Epoch: 3 Train Loss: 0.6928 Acc: 0.5158 Pre: 0.5082 Recall: 0.9821 F1: 0.6698 Train AUC: 0.5312 Val AUC: 0.5179 Val PRC: 0.5177 Time: 0.73\n",
      "Epoch: 4 Train Loss: 0.6875 Acc: 0.5210 Pre: 0.5109 Recall: 0.9853 F1: 0.6729 Train AUC: 0.5574 Val AUC: 0.5467 Val PRC: 0.5448 Time: 0.76\n",
      "Epoch: 5 Train Loss: 0.6850 Acc: 0.5257 Pre: 0.5134 Recall: 0.9842 F1: 0.6748 Train AUC: 0.5677 Val AUC: 0.5494 Val PRC: 0.5289 Time: 0.78\n",
      "Epoch: 6 Train Loss: 0.6935 Acc: 0.5037 Pre: 0.5018 Recall: 0.9989 F1: 0.6681 Train AUC: 0.5670 Val AUC: 0.5624 Val PRC: 0.5960 Time: 0.78\n",
      "Epoch: 7 Train Loss: 0.6837 Acc: 0.5473 Pre: 0.5257 Recall: 0.9674 F1: 0.6812 Train AUC: 0.5759 Val AUC: 0.5735 Val PRC: 0.5595 Time: 0.79\n",
      "Epoch: 8 Train Loss: 0.6706 Acc: 0.5578 Pre: 0.5321 Recall: 0.9580 F1: 0.6842 Train AUC: 0.6592 Val AUC: 0.6381 Val PRC: 0.6333 Time: 0.72\n",
      "Epoch: 9 Train Loss: 0.6807 Acc: 0.5168 Pre: 0.5087 Recall: 0.9790 F1: 0.6695 Train AUC: 0.6116 Val AUC: 0.6230 Val PRC: 0.6326 Time: 0.71\n",
      "Epoch: 10 Train Loss: 0.6653 Acc: 0.5657 Pre: 0.5361 Recall: 0.9758 F1: 0.6920 Train AUC: 0.6850 Val AUC: 0.6808 Val PRC: 0.6711 Time: 0.72\n",
      "Epoch: 11 Train Loss: 0.6606 Acc: 0.6024 Pre: 0.5589 Recall: 0.9716 F1: 0.7096 Train AUC: 0.7005 Val AUC: 0.7000 Val PRC: 0.6738 Time: 0.71\n",
      "Epoch: 12 Train Loss: 0.6499 Acc: 0.6749 Pre: 0.6206 Recall: 0.9002 F1: 0.7347 Train AUC: 0.7664 Val AUC: 0.7742 Val PRC: 0.7683 Time: 0.71\n",
      "Epoch: 13 Train Loss: 0.6516 Acc: 0.6360 Pre: 0.5905 Recall: 0.8876 F1: 0.7092 Train AUC: 0.7396 Val AUC: 0.7244 Val PRC: 0.7160 Time: 0.72\n",
      "Epoch: 14 Train Loss: 0.6400 Acc: 0.6213 Pre: 0.5727 Recall: 0.9559 F1: 0.7163 Train AUC: 0.7663 Val AUC: 0.7755 Val PRC: 0.7885 Time: 0.71\n",
      "Epoch: 15 Train Loss: 0.6331 Acc: 0.6775 Pre: 0.6286 Recall: 0.8676 F1: 0.7290 Train AUC: 0.7936 Val AUC: 0.7924 Val PRC: 0.7970 Time: 0.71\n",
      "Epoch: 16 Train Loss: 0.6294 Acc: 0.6639 Pre: 0.6180 Recall: 0.8582 F1: 0.7186 Train AUC: 0.7855 Val AUC: 0.7862 Val PRC: 0.8078 Time: 0.72\n",
      "Epoch: 17 Train Loss: 0.6163 Acc: 0.6612 Pre: 0.6145 Recall: 0.8655 F1: 0.7187 Train AUC: 0.8016 Val AUC: 0.8006 Val PRC: 0.8282 Time: 0.69\n",
      "Epoch: 18 Train Loss: 0.6103 Acc: 0.6192 Pre: 0.5702 Recall: 0.9685 F1: 0.7178 Train AUC: 0.7984 Val AUC: 0.7929 Val PRC: 0.8186 Time: 0.71\n",
      "Epoch: 19 Train Loss: 0.6083 Acc: 0.6985 Pre: 0.6449 Recall: 0.8834 F1: 0.7456 Train AUC: 0.8188 Val AUC: 0.8198 Val PRC: 0.8304 Time: 0.72\n",
      "Epoch: 20 Train Loss: 0.5969 Acc: 0.7012 Pre: 0.6670 Recall: 0.8036 F1: 0.7289 Train AUC: 0.8126 Val AUC: 0.8068 Val PRC: 0.8288 Time: 0.71\n",
      "Epoch: 21 Train Loss: 0.5858 Acc: 0.6807 Pre: 0.6259 Recall: 0.8981 F1: 0.7377 Train AUC: 0.8217 Val AUC: 0.8184 Val PRC: 0.8421 Time: 0.71\n",
      "Epoch: 22 Train Loss: 0.5759 Acc: 0.7400 Pre: 0.7418 Recall: 0.7363 F1: 0.7391 Train AUC: 0.8282 Val AUC: 0.8250 Val PRC: 0.8472 Time: 0.72\n",
      "Epoch: 23 Train Loss: 0.5720 Acc: 0.6959 Pre: 0.6532 Recall: 0.8351 F1: 0.7331 Train AUC: 0.8201 Val AUC: 0.8173 Val PRC: 0.8415 Time: 0.71\n",
      "Epoch: 24 Train Loss: 0.5667 Acc: 0.6901 Pre: 0.6382 Recall: 0.8782 F1: 0.7392 Train AUC: 0.8288 Val AUC: 0.8271 Val PRC: 0.8477 Time: 0.69\n",
      "Epoch: 25 Train Loss: 0.5588 Acc: 0.7253 Pre: 0.6959 Recall: 0.8004 F1: 0.7445 Train AUC: 0.8329 Val AUC: 0.8310 Val PRC: 0.8526 Time: 0.73\n",
      "Epoch: 26 Train Loss: 0.5571 Acc: 0.7206 Pre: 0.6688 Recall: 0.8739 F1: 0.7577 Train AUC: 0.8347 Val AUC: 0.8398 Val PRC: 0.8558 Time: 0.71\n",
      "Epoch: 27 Train Loss: 0.5401 Acc: 0.7075 Pre: 0.6518 Recall: 0.8908 F1: 0.7528 Train AUC: 0.8322 Val AUC: 0.8326 Val PRC: 0.8495 Time: 0.71\n",
      "Epoch: 28 Train Loss: 0.5328 Acc: 0.7269 Pre: 0.6932 Recall: 0.8141 F1: 0.7488 Train AUC: 0.8394 Val AUC: 0.8374 Val PRC: 0.8540 Time: 0.71\n",
      "Epoch: 29 Train Loss: 0.5245 Acc: 0.7148 Pre: 0.6667 Recall: 0.8592 F1: 0.7508 Train AUC: 0.8377 Val AUC: 0.8395 Val PRC: 0.8619 Time: 0.71\n",
      "Epoch: 30 Train Loss: 0.5150 Acc: 0.7117 Pre: 0.6600 Recall: 0.8729 F1: 0.7517 Train AUC: 0.8434 Val AUC: 0.8462 Val PRC: 0.8644 Time: 0.71\n",
      "Epoch: 31 Train Loss: 0.4948 Acc: 0.7532 Pre: 0.7405 Recall: 0.7794 F1: 0.7595 Train AUC: 0.8581 Val AUC: 0.8580 Val PRC: 0.8780 Time: 0.74\n",
      "Epoch: 32 Train Loss: 0.4977 Acc: 0.7274 Pre: 0.6843 Recall: 0.8445 F1: 0.7560 Train AUC: 0.8510 Val AUC: 0.8498 Val PRC: 0.8719 Time: 0.71\n",
      "Epoch: 33 Train Loss: 0.5013 Acc: 0.7190 Pre: 0.6697 Recall: 0.8645 F1: 0.7547 Train AUC: 0.8429 Val AUC: 0.8431 Val PRC: 0.8634 Time: 0.74\n",
      "Epoch: 34 Train Loss: 0.4812 Acc: 0.7300 Pre: 0.6878 Recall: 0.8424 F1: 0.7573 Train AUC: 0.8559 Val AUC: 0.8534 Val PRC: 0.8762 Time: 0.71\n",
      "Epoch: 35 Train Loss: 0.4831 Acc: 0.7353 Pre: 0.6870 Recall: 0.8645 F1: 0.7656 Train AUC: 0.8557 Val AUC: 0.8608 Val PRC: 0.8753 Time: 0.71\n",
      "Epoch: 36 Train Loss: 0.4813 Acc: 0.7642 Pre: 0.7553 Recall: 0.7815 F1: 0.7682 Train AUC: 0.8566 Val AUC: 0.8615 Val PRC: 0.8802 Time: 0.71\n",
      "Epoch: 37 Train Loss: 0.4777 Acc: 0.7547 Pre: 0.7375 Recall: 0.7910 F1: 0.7633 Train AUC: 0.8483 Val AUC: 0.8593 Val PRC: 0.8796 Time: 0.72\n",
      "Epoch: 38 Train Loss: 0.4667 Acc: 0.7463 Pre: 0.7190 Recall: 0.8088 F1: 0.7612 Train AUC: 0.8556 Val AUC: 0.8594 Val PRC: 0.8792 Time: 0.72\n",
      "Epoch: 39 Train Loss: 0.4571 Acc: 0.7631 Pre: 0.7267 Recall: 0.8435 F1: 0.7807 Train AUC: 0.8674 Val AUC: 0.8735 Val PRC: 0.8898 Time: 0.71\n",
      "Epoch: 40 Train Loss: 0.4483 Acc: 0.7521 Pre: 0.7139 Recall: 0.8414 F1: 0.7724 Train AUC: 0.8657 Val AUC: 0.8726 Val PRC: 0.8926 Time: 0.70\n",
      "Epoch: 41 Train Loss: 0.4463 Acc: 0.7784 Pre: 0.7619 Recall: 0.8099 F1: 0.7851 Train AUC: 0.8672 Val AUC: 0.8814 Val PRC: 0.9007 Time: 0.71\n",
      "Epoch: 42 Train Loss: 0.4427 Acc: 0.7458 Pre: 0.6986 Recall: 0.8645 F1: 0.7728 Train AUC: 0.8697 Val AUC: 0.8746 Val PRC: 0.8934 Time: 0.70\n",
      "Epoch: 43 Train Loss: 0.4446 Acc: 0.7731 Pre: 0.7471 Recall: 0.8256 F1: 0.7844 Train AUC: 0.8704 Val AUC: 0.8816 Val PRC: 0.8983 Time: 0.73\n",
      "Epoch: 44 Train Loss: 0.4419 Acc: 0.7857 Pre: 0.7636 Recall: 0.8277 F1: 0.7944 Train AUC: 0.8702 Val AUC: 0.8863 Val PRC: 0.9040 Time: 0.72\n",
      "Epoch: 45 Train Loss: 0.4231 Acc: 0.8130 Pre: 0.9105 Recall: 0.6943 F1: 0.7878 Train AUC: 0.8784 Val AUC: 0.8861 Val PRC: 0.9043 Time: 0.71\n",
      "Epoch: 46 Train Loss: 0.4242 Acc: 0.8162 Pre: 0.8961 Recall: 0.7153 F1: 0.7956 Train AUC: 0.8812 Val AUC: 0.8927 Val PRC: 0.9095 Time: 0.70\n",
      "Epoch: 47 Train Loss: 0.4263 Acc: 0.8151 Pre: 0.8817 Recall: 0.7279 F1: 0.7975 Train AUC: 0.8749 Val AUC: 0.8906 Val PRC: 0.9090 Time: 0.72\n",
      "Epoch: 48 Train Loss: 0.4351 Acc: 0.8178 Pre: 0.9340 Recall: 0.6838 F1: 0.7896 Train AUC: 0.8715 Val AUC: 0.8865 Val PRC: 0.9053 Time: 0.70\n",
      "Epoch: 49 Train Loss: 0.4176 Acc: 0.8030 Pre: 0.8021 Recall: 0.8046 F1: 0.8034 Train AUC: 0.8858 Val AUC: 0.8964 Val PRC: 0.9111 Time: 0.70\n",
      "Epoch: 50 Train Loss: 0.4093 Acc: 0.8025 Pre: 0.7869 Recall: 0.8298 F1: 0.8078 Train AUC: 0.8900 Val AUC: 0.9019 Val PRC: 0.9170 Time: 0.70\n",
      "Epoch: 51 Train Loss: 0.4085 Acc: 0.8172 Pre: 0.8166 Recall: 0.8183 F1: 0.8174 Train AUC: 0.8913 Val AUC: 0.9062 Val PRC: 0.9198 Time: 0.70\n",
      "Epoch: 52 Train Loss: 0.4043 Acc: 0.8293 Pre: 0.9014 Recall: 0.7395 F1: 0.8125 Train AUC: 0.8934 Val AUC: 0.9033 Val PRC: 0.9166 Time: 0.69\n",
      "Epoch: 53 Train Loss: 0.3981 Acc: 0.8314 Pre: 0.9019 Recall: 0.7437 F1: 0.8152 Train AUC: 0.8958 Val AUC: 0.9066 Val PRC: 0.9218 Time: 0.72\n",
      "Epoch: 54 Train Loss: 0.4018 Acc: 0.8277 Pre: 0.8679 Recall: 0.7731 F1: 0.8178 Train AUC: 0.8947 Val AUC: 0.9087 Val PRC: 0.9235 Time: 0.71\n",
      "Epoch: 55 Train Loss: 0.3924 Acc: 0.8262 Pre: 0.8090 Recall: 0.8540 F1: 0.8309 Train AUC: 0.9002 Val AUC: 0.9182 Val PRC: 0.9308 Time: 0.72\n",
      "Epoch: 56 Train Loss: 0.3832 Acc: 0.8445 Pre: 0.9000 Recall: 0.7752 F1: 0.8330 Train AUC: 0.9044 Val AUC: 0.9140 Val PRC: 0.9280 Time: 0.94\n",
      "Epoch: 57 Train Loss: 0.3821 Acc: 0.8466 Pre: 0.8929 Recall: 0.7878 F1: 0.8371 Train AUC: 0.9065 Val AUC: 0.9198 Val PRC: 0.9328 Time: 0.70\n",
      "Epoch: 58 Train Loss: 0.3892 Acc: 0.8493 Pre: 0.9120 Recall: 0.7731 F1: 0.8368 Train AUC: 0.9036 Val AUC: 0.9200 Val PRC: 0.9321 Time: 0.71\n",
      "Epoch: 59 Train Loss: 0.3787 Acc: 0.8456 Pre: 0.8808 Recall: 0.7994 F1: 0.8381 Train AUC: 0.9088 Val AUC: 0.9206 Val PRC: 0.9337 Time: 0.71\n",
      "Epoch: 60 Train Loss: 0.3791 Acc: 0.8498 Pre: 0.8784 Recall: 0.8120 F1: 0.8439 Train AUC: 0.9076 Val AUC: 0.9253 Val PRC: 0.9368 Time: 0.73\n",
      "Epoch: 61 Train Loss: 0.3780 Acc: 0.8472 Pre: 0.8693 Recall: 0.8172 F1: 0.8424 Train AUC: 0.9093 Val AUC: 0.9235 Val PRC: 0.9353 Time: 0.74\n",
      "Epoch: 62 Train Loss: 0.3802 Acc: 0.8424 Pre: 0.8273 Recall: 0.8655 F1: 0.8460 Train AUC: 0.9101 Val AUC: 0.9262 Val PRC: 0.9361 Time: 0.71\n",
      "Epoch: 63 Train Loss: 0.3724 Acc: 0.8514 Pre: 0.9229 Recall: 0.7668 F1: 0.8376 Train AUC: 0.9112 Val AUC: 0.9237 Val PRC: 0.9359 Time: 0.72\n",
      "Epoch: 64 Train Loss: 0.3680 Acc: 0.8440 Pre: 0.8311 Recall: 0.8634 F1: 0.8470 Train AUC: 0.9158 Val AUC: 0.9286 Val PRC: 0.9392 Time: 0.73\n",
      "Epoch: 65 Train Loss: 0.3563 Acc: 0.8571 Pre: 0.8837 Recall: 0.8225 F1: 0.8520 Train AUC: 0.9206 Val AUC: 0.9309 Val PRC: 0.9404 Time: 0.72\n",
      "Epoch: 66 Train Loss: 0.3668 Acc: 0.8550 Pre: 0.8789 Recall: 0.8235 F1: 0.8503 Train AUC: 0.9147 Val AUC: 0.9296 Val PRC: 0.9401 Time: 0.70\n",
      "Epoch: 67 Train Loss: 0.3548 Acc: 0.8514 Pre: 0.8763 Recall: 0.8183 F1: 0.8463 Train AUC: 0.9193 Val AUC: 0.9283 Val PRC: 0.9396 Time: 0.71\n",
      "Epoch: 68 Train Loss: 0.3509 Acc: 0.8619 Pre: 0.8798 Recall: 0.8382 F1: 0.8585 Train AUC: 0.9203 Val AUC: 0.9330 Val PRC: 0.9431 Time: 0.71\n",
      "Epoch: 69 Train Loss: 0.3548 Acc: 0.8508 Pre: 0.8340 Recall: 0.8761 F1: 0.8545 Train AUC: 0.9211 Val AUC: 0.9341 Val PRC: 0.9434 Time: 0.71\n",
      "Epoch: 70 Train Loss: 0.3535 Acc: 0.8519 Pre: 0.8534 Recall: 0.8498 F1: 0.8516 Train AUC: 0.9216 Val AUC: 0.9333 Val PRC: 0.9429 Time: 0.70\n",
      "Epoch: 71 Train Loss: 0.3473 Acc: 0.8477 Pre: 0.8567 Recall: 0.8351 F1: 0.8457 Train AUC: 0.9230 Val AUC: 0.9297 Val PRC: 0.9404 Time: 0.71\n",
      "Epoch: 72 Train Loss: 0.3415 Acc: 0.8619 Pre: 0.8600 Recall: 0.8645 F1: 0.8622 Train AUC: 0.9273 Val AUC: 0.9351 Val PRC: 0.9450 Time: 0.69\n",
      "Epoch: 73 Train Loss: 0.3457 Acc: 0.8713 Pre: 0.9068 Recall: 0.8277 F1: 0.8655 Train AUC: 0.9257 Val AUC: 0.9385 Val PRC: 0.9450 Time: 0.70\n",
      "Epoch: 74 Train Loss: 0.3276 Acc: 0.8655 Pre: 0.8633 Recall: 0.8687 F1: 0.8660 Train AUC: 0.9338 Val AUC: 0.9385 Val PRC: 0.9463 Time: 0.70\n",
      "Epoch: 75 Train Loss: 0.3450 Acc: 0.8687 Pre: 0.8944 Recall: 0.8361 F1: 0.8643 Train AUC: 0.9266 Val AUC: 0.9417 Val PRC: 0.9497 Time: 0.70\n",
      "Epoch: 76 Train Loss: 0.3358 Acc: 0.8687 Pre: 0.9120 Recall: 0.8162 F1: 0.8614 Train AUC: 0.9295 Val AUC: 0.9395 Val PRC: 0.9477 Time: 0.70\n",
      "Epoch: 77 Train Loss: 0.3230 Acc: 0.8608 Pre: 0.8523 Recall: 0.8729 F1: 0.8625 Train AUC: 0.9347 Val AUC: 0.9398 Val PRC: 0.9476 Time: 0.70\n",
      "Epoch: 78 Train Loss: 0.3379 Acc: 0.8708 Pre: 0.8662 Recall: 0.8771 F1: 0.8716 Train AUC: 0.9294 Val AUC: 0.9419 Val PRC: 0.9469 Time: 0.72\n",
      "Epoch: 79 Train Loss: 0.3363 Acc: 0.8734 Pre: 0.8723 Recall: 0.8750 F1: 0.8736 Train AUC: 0.9300 Val AUC: 0.9438 Val PRC: 0.9512 Time: 0.71\n",
      "Epoch: 80 Train Loss: 0.3279 Acc: 0.8761 Pre: 0.8883 Recall: 0.8603 F1: 0.8741 Train AUC: 0.9356 Val AUC: 0.9446 Val PRC: 0.9497 Time: 0.71\n",
      "Epoch: 81 Train Loss: 0.3210 Acc: 0.8771 Pre: 0.8835 Recall: 0.8687 F1: 0.8761 Train AUC: 0.9388 Val AUC: 0.9464 Val PRC: 0.9537 Time: 0.72\n",
      "Epoch: 82 Train Loss: 0.3272 Acc: 0.8676 Pre: 0.8564 Recall: 0.8834 F1: 0.8697 Train AUC: 0.9363 Val AUC: 0.9450 Val PRC: 0.9496 Time: 0.69\n",
      "Epoch: 83 Train Loss: 0.3189 Acc: 0.8803 Pre: 0.9058 Recall: 0.8487 F1: 0.8764 Train AUC: 0.9386 Val AUC: 0.9472 Val PRC: 0.9514 Time: 0.70\n",
      "Epoch: 84 Train Loss: 0.3120 Acc: 0.8808 Pre: 0.8820 Recall: 0.8792 F1: 0.8806 Train AUC: 0.9426 Val AUC: 0.9482 Val PRC: 0.9544 Time: 0.73\n",
      "Epoch: 85 Train Loss: 0.3105 Acc: 0.8745 Pre: 0.8821 Recall: 0.8645 F1: 0.8732 Train AUC: 0.9412 Val AUC: 0.9474 Val PRC: 0.9539 Time: 0.73\n",
      "Epoch: 86 Train Loss: 0.3173 Acc: 0.8761 Pre: 0.8698 Recall: 0.8845 F1: 0.8771 Train AUC: 0.9398 Val AUC: 0.9469 Val PRC: 0.9507 Time: 0.71\n",
      "Epoch: 87 Train Loss: 0.3116 Acc: 0.8745 Pre: 0.8465 Recall: 0.9149 F1: 0.8794 Train AUC: 0.9426 Val AUC: 0.9491 Val PRC: 0.9533 Time: 0.71\n",
      "Epoch: 88 Train Loss: 0.3073 Acc: 0.8834 Pre: 0.8985 Recall: 0.8645 F1: 0.8812 Train AUC: 0.9437 Val AUC: 0.9523 Val PRC: 0.9543 Time: 0.70\n",
      "Epoch: 89 Train Loss: 0.3096 Acc: 0.8892 Pre: 0.9177 Recall: 0.8550 F1: 0.8853 Train AUC: 0.9421 Val AUC: 0.9516 Val PRC: 0.9557 Time: 0.70\n",
      "Epoch: 90 Train Loss: 0.3018 Acc: 0.8824 Pre: 0.9118 Recall: 0.8466 F1: 0.8780 Train AUC: 0.9452 Val AUC: 0.9528 Val PRC: 0.9567 Time: 0.75\n",
      "Epoch: 91 Train Loss: 0.2944 Acc: 0.8871 Pre: 0.8967 Recall: 0.8750 F1: 0.8857 Train AUC: 0.9489 Val AUC: 0.9530 Val PRC: 0.9550 Time: 0.71\n",
      "Epoch: 92 Train Loss: 0.2999 Acc: 0.8850 Pre: 0.8728 Recall: 0.9013 F1: 0.8868 Train AUC: 0.9466 Val AUC: 0.9557 Val PRC: 0.9576 Time: 0.71\n",
      "Epoch: 93 Train Loss: 0.2972 Acc: 0.8839 Pre: 0.9021 Recall: 0.8613 F1: 0.8812 Train AUC: 0.9472 Val AUC: 0.9519 Val PRC: 0.9544 Time: 0.73\n",
      "Epoch: 94 Train Loss: 0.2951 Acc: 0.8845 Pre: 0.8944 Recall: 0.8718 F1: 0.8830 Train AUC: 0.9474 Val AUC: 0.9550 Val PRC: 0.9569 Time: 0.72\n",
      "Epoch: 95 Train Loss: 0.2906 Acc: 0.8965 Pre: 0.9227 Recall: 0.8655 F1: 0.8932 Train AUC: 0.9496 Val AUC: 0.9570 Val PRC: 0.9594 Time: 0.72\n",
      "Epoch: 96 Train Loss: 0.2849 Acc: 0.8955 Pre: 0.9133 Recall: 0.8739 F1: 0.8932 Train AUC: 0.9517 Val AUC: 0.9584 Val PRC: 0.9601 Time: 0.72\n",
      "Epoch: 97 Train Loss: 0.2840 Acc: 0.8887 Pre: 0.8715 Recall: 0.9118 F1: 0.8912 Train AUC: 0.9526 Val AUC: 0.9577 Val PRC: 0.9604 Time: 0.72\n",
      "Epoch: 98 Train Loss: 0.2910 Acc: 0.8913 Pre: 0.8992 Recall: 0.8813 F1: 0.8902 Train AUC: 0.9499 Val AUC: 0.9563 Val PRC: 0.9602 Time: 0.73\n",
      "Epoch: 99 Train Loss: 0.2884 Acc: 0.8855 Pre: 0.8955 Recall: 0.8729 F1: 0.8840 Train AUC: 0.9507 Val AUC: 0.9550 Val PRC: 0.9547 Time: 0.71\n",
      "Epoch: 100 Train Loss: 0.2889 Acc: 0.8934 Pre: 0.8749 Recall: 0.9181 F1: 0.8960 Train AUC: 0.9508 Val AUC: 0.9578 Val PRC: 0.9573 Time: 0.72\n",
      "Epoch: 101 Train Loss: 0.2833 Acc: 0.8950 Pre: 0.9060 Recall: 0.8813 F1: 0.8935 Train AUC: 0.9532 Val AUC: 0.9602 Val PRC: 0.9605 Time: 0.71\n",
      "Epoch: 102 Train Loss: 0.2831 Acc: 0.8934 Pre: 0.8988 Recall: 0.8866 F1: 0.8926 Train AUC: 0.9530 Val AUC: 0.9587 Val PRC: 0.9585 Time: 0.72\n",
      "Epoch: 103 Train Loss: 0.2794 Acc: 0.8923 Pre: 0.8952 Recall: 0.8887 F1: 0.8919 Train AUC: 0.9541 Val AUC: 0.9606 Val PRC: 0.9617 Time: 0.70\n",
      "Epoch: 104 Train Loss: 0.2804 Acc: 0.8939 Pre: 0.8890 Recall: 0.9002 F1: 0.8946 Train AUC: 0.9541 Val AUC: 0.9607 Val PRC: 0.9595 Time: 0.74\n",
      "Epoch: 105 Train Loss: 0.2848 Acc: 0.8929 Pre: 0.8904 Recall: 0.8960 F1: 0.8932 Train AUC: 0.9522 Val AUC: 0.9607 Val PRC: 0.9614 Time: 0.72\n",
      "Epoch: 106 Train Loss: 0.2835 Acc: 0.8934 Pre: 0.9120 Recall: 0.8708 F1: 0.8909 Train AUC: 0.9531 Val AUC: 0.9617 Val PRC: 0.9591 Time: 0.72\n",
      "Epoch: 107 Train Loss: 0.2766 Acc: 0.8955 Pre: 0.8769 Recall: 0.9202 F1: 0.8980 Train AUC: 0.9547 Val AUC: 0.9621 Val PRC: 0.9617 Time: 0.71\n",
      "Epoch: 108 Train Loss: 0.2799 Acc: 0.8929 Pre: 0.9030 Recall: 0.8803 F1: 0.8915 Train AUC: 0.9549 Val AUC: 0.9617 Val PRC: 0.9636 Time: 0.72\n",
      "Epoch: 109 Train Loss: 0.2607 Acc: 0.8876 Pre: 0.8646 Recall: 0.9191 F1: 0.8910 Train AUC: 0.9600 Val AUC: 0.9614 Val PRC: 0.9627 Time: 0.71\n",
      "Epoch: 110 Train Loss: 0.2657 Acc: 0.8981 Pre: 0.9138 Recall: 0.8792 F1: 0.8961 Train AUC: 0.9588 Val AUC: 0.9644 Val PRC: 0.9661 Time: 0.72\n",
      "Epoch: 111 Train Loss: 0.2681 Acc: 0.8976 Pre: 0.9014 Recall: 0.8929 F1: 0.8971 Train AUC: 0.9575 Val AUC: 0.9637 Val PRC: 0.9651 Time: 0.71\n",
      "Epoch: 112 Train Loss: 0.2750 Acc: 0.8913 Pre: 0.8933 Recall: 0.8887 F1: 0.8910 Train AUC: 0.9546 Val AUC: 0.9619 Val PRC: 0.9621 Time: 0.73\n",
      "Epoch: 113 Train Loss: 0.2575 Acc: 0.8923 Pre: 0.8746 Recall: 0.9160 F1: 0.8948 Train AUC: 0.9612 Val AUC: 0.9650 Val PRC: 0.9655 Time: 0.71\n",
      "Epoch: 114 Train Loss: 0.2722 Acc: 0.8902 Pre: 0.8898 Recall: 0.8908 F1: 0.8903 Train AUC: 0.9563 Val AUC: 0.9625 Val PRC: 0.9630 Time: 0.71\n",
      "Epoch: 115 Train Loss: 0.2686 Acc: 0.8971 Pre: 0.9004 Recall: 0.8929 F1: 0.8966 Train AUC: 0.9572 Val AUC: 0.9641 Val PRC: 0.9649 Time: 0.72\n",
      "Epoch: 116 Train Loss: 0.2637 Acc: 0.8955 Pre: 0.8799 Recall: 0.9160 F1: 0.8976 Train AUC: 0.9592 Val AUC: 0.9642 Val PRC: 0.9655 Time: 0.72\n",
      "Epoch: 117 Train Loss: 0.2596 Acc: 0.9002 Pre: 0.8994 Recall: 0.9013 F1: 0.9003 Train AUC: 0.9604 Val AUC: 0.9653 Val PRC: 0.9638 Time: 0.71\n",
      "Epoch: 118 Train Loss: 0.2554 Acc: 0.9023 Pre: 0.8932 Recall: 0.9139 F1: 0.9034 Train AUC: 0.9626 Val AUC: 0.9656 Val PRC: 0.9644 Time: 0.73\n",
      "Epoch: 119 Train Loss: 0.2662 Acc: 0.9065 Pre: 0.9161 Recall: 0.8950 F1: 0.9054 Train AUC: 0.9580 Val AUC: 0.9671 Val PRC: 0.9650 Time: 0.72\n",
      "Epoch: 120 Train Loss: 0.2644 Acc: 0.9028 Pre: 0.9041 Recall: 0.9013 F1: 0.9027 Train AUC: 0.9588 Val AUC: 0.9677 Val PRC: 0.9686 Time: 0.73\n",
      "Epoch: 121 Train Loss: 0.2511 Acc: 0.9039 Pre: 0.9166 Recall: 0.8887 F1: 0.9024 Train AUC: 0.9624 Val AUC: 0.9666 Val PRC: 0.9639 Time: 0.72\n",
      "Epoch: 122 Train Loss: 0.2536 Acc: 0.8997 Pre: 0.8801 Recall: 0.9254 F1: 0.9022 Train AUC: 0.9622 Val AUC: 0.9668 Val PRC: 0.9651 Time: 0.74\n",
      "Epoch: 123 Train Loss: 0.2507 Acc: 0.9070 Pre: 0.9263 Recall: 0.8845 F1: 0.9049 Train AUC: 0.9625 Val AUC: 0.9679 Val PRC: 0.9659 Time: 0.71\n",
      "Epoch: 124 Train Loss: 0.2566 Acc: 0.8981 Pre: 0.8867 Recall: 0.9128 F1: 0.8996 Train AUC: 0.9602 Val AUC: 0.9671 Val PRC: 0.9673 Time: 0.72\n",
      "Epoch: 125 Train Loss: 0.2536 Acc: 0.9018 Pre: 0.9073 Recall: 0.8950 F1: 0.9011 Train AUC: 0.9621 Val AUC: 0.9661 Val PRC: 0.9660 Time: 0.73\n",
      "Epoch: 126 Train Loss: 0.2529 Acc: 0.9065 Pre: 0.9300 Recall: 0.8792 F1: 0.9039 Train AUC: 0.9622 Val AUC: 0.9681 Val PRC: 0.9693 Time: 0.71\n",
      "Epoch: 127 Train Loss: 0.2505 Acc: 0.9086 Pre: 0.9192 Recall: 0.8960 F1: 0.9074 Train AUC: 0.9621 Val AUC: 0.9692 Val PRC: 0.9707 Time: 0.72\n",
      "Epoch: 128 Train Loss: 0.2527 Acc: 0.9039 Pre: 0.9077 Recall: 0.8992 F1: 0.9034 Train AUC: 0.9617 Val AUC: 0.9683 Val PRC: 0.9689 Time: 0.72\n",
      "Epoch: 129 Train Loss: 0.2584 Acc: 0.9028 Pre: 0.9067 Recall: 0.8981 F1: 0.9024 Train AUC: 0.9598 Val AUC: 0.9682 Val PRC: 0.9679 Time: 0.71\n",
      "Epoch: 130 Train Loss: 0.2459 Acc: 0.9039 Pre: 0.9043 Recall: 0.9034 F1: 0.9038 Train AUC: 0.9636 Val AUC: 0.9694 Val PRC: 0.9690 Time: 0.71\n",
      "Epoch: 131 Train Loss: 0.2516 Acc: 0.9013 Pre: 0.8859 Recall: 0.9212 F1: 0.9032 Train AUC: 0.9619 Val AUC: 0.9692 Val PRC: 0.9694 Time: 0.70\n",
      "Epoch: 132 Train Loss: 0.2403 Acc: 0.9018 Pre: 0.8875 Recall: 0.9202 F1: 0.9036 Train AUC: 0.9656 Val AUC: 0.9678 Val PRC: 0.9674 Time: 0.73\n",
      "Epoch: 133 Train Loss: 0.2396 Acc: 0.9091 Pre: 0.9104 Recall: 0.9076 F1: 0.9090 Train AUC: 0.9652 Val AUC: 0.9690 Val PRC: 0.9692 Time: 0.72\n",
      "Epoch: 134 Train Loss: 0.2445 Acc: 0.9091 Pre: 0.9166 Recall: 0.9002 F1: 0.9083 Train AUC: 0.9640 Val AUC: 0.9689 Val PRC: 0.9692 Time: 0.70\n",
      "Epoch: 135 Train Loss: 0.2370 Acc: 0.9097 Pre: 0.9324 Recall: 0.8834 F1: 0.9072 Train AUC: 0.9660 Val AUC: 0.9699 Val PRC: 0.9707 Time: 0.70\n",
      "Epoch: 136 Train Loss: 0.2383 Acc: 0.9076 Pre: 0.9369 Recall: 0.8739 F1: 0.9043 Train AUC: 0.9657 Val AUC: 0.9678 Val PRC: 0.9673 Time: 0.72\n",
      "Epoch: 137 Train Loss: 0.2340 Acc: 0.9018 Pre: 0.9031 Recall: 0.9002 F1: 0.9016 Train AUC: 0.9673 Val AUC: 0.9678 Val PRC: 0.9688 Time: 0.71\n",
      "Epoch: 138 Train Loss: 0.2386 Acc: 0.9081 Pre: 0.9191 Recall: 0.8950 F1: 0.9069 Train AUC: 0.9655 Val AUC: 0.9688 Val PRC: 0.9685 Time: 0.70\n",
      "Epoch: 139 Train Loss: 0.2379 Acc: 0.9039 Pre: 0.9130 Recall: 0.8929 F1: 0.9028 Train AUC: 0.9657 Val AUC: 0.9685 Val PRC: 0.9669 Time: 0.73\n",
      "Epoch: 140 Train Loss: 0.2418 Acc: 0.9102 Pre: 0.9258 Recall: 0.8918 F1: 0.9085 Train AUC: 0.9648 Val AUC: 0.9704 Val PRC: 0.9708 Time: 0.72\n",
      "Epoch: 141 Train Loss: 0.2230 Acc: 0.9018 Pre: 0.8821 Recall: 0.9275 F1: 0.9042 Train AUC: 0.9703 Val AUC: 0.9714 Val PRC: 0.9713 Time: 0.72\n",
      "Epoch: 142 Train Loss: 0.2349 Acc: 0.9081 Pre: 0.8928 Recall: 0.9275 F1: 0.9098 Train AUC: 0.9664 Val AUC: 0.9713 Val PRC: 0.9672 Time: 0.70\n",
      "Epoch: 143 Train Loss: 0.2370 Acc: 0.9091 Pre: 0.9062 Recall: 0.9128 F1: 0.9095 Train AUC: 0.9664 Val AUC: 0.9708 Val PRC: 0.9704 Time: 0.73\n",
      "Epoch: 144 Train Loss: 0.2364 Acc: 0.9091 Pre: 0.9113 Recall: 0.9065 F1: 0.9089 Train AUC: 0.9660 Val AUC: 0.9691 Val PRC: 0.9676 Time: 0.70\n",
      "Epoch: 145 Train Loss: 0.2389 Acc: 0.9076 Pre: 0.9093 Recall: 0.9055 F1: 0.9074 Train AUC: 0.9657 Val AUC: 0.9698 Val PRC: 0.9702 Time: 0.71\n",
      "Epoch: 146 Train Loss: 0.2266 Acc: 0.9049 Pre: 0.9088 Recall: 0.9002 F1: 0.9045 Train AUC: 0.9688 Val AUC: 0.9701 Val PRC: 0.9700 Time: 0.71\n",
      "Epoch: 147 Train Loss: 0.2358 Acc: 0.9055 Pre: 0.8719 Recall: 0.9506 F1: 0.9095 Train AUC: 0.9664 Val AUC: 0.9707 Val PRC: 0.9670 Time: 0.71\n",
      "Epoch: 148 Train Loss: 0.2400 Acc: 0.9060 Pre: 0.8924 Recall: 0.9233 F1: 0.9076 Train AUC: 0.9646 Val AUC: 0.9692 Val PRC: 0.9683 Time: 0.70\n",
      "Epoch: 149 Train Loss: 0.2313 Acc: 0.9060 Pre: 0.8900 Recall: 0.9265 F1: 0.9079 Train AUC: 0.9675 Val AUC: 0.9718 Val PRC: 0.9714 Time: 0.75\n",
      "Epoch: 150 Train Loss: 0.2221 Acc: 0.9118 Pre: 0.9161 Recall: 0.9065 F1: 0.9113 Train AUC: 0.9702 Val AUC: 0.9719 Val PRC: 0.9715 Time: 0.71\n",
      "Epoch: 151 Train Loss: 0.2313 Acc: 0.9128 Pre: 0.9128 Recall: 0.9128 F1: 0.9128 Train AUC: 0.9675 Val AUC: 0.9727 Val PRC: 0.9724 Time: 0.72\n",
      "Epoch: 152 Train Loss: 0.2245 Acc: 0.9186 Pre: 0.9403 Recall: 0.8939 F1: 0.9165 Train AUC: 0.9695 Val AUC: 0.9734 Val PRC: 0.9736 Time: 0.72\n",
      "Epoch: 153 Train Loss: 0.2304 Acc: 0.9107 Pre: 0.8957 Recall: 0.9296 F1: 0.9124 Train AUC: 0.9680 Val AUC: 0.9740 Val PRC: 0.9721 Time: 0.74\n",
      "Epoch: 154 Train Loss: 0.2192 Acc: 0.9128 Pre: 0.9235 Recall: 0.9002 F1: 0.9117 Train AUC: 0.9708 Val AUC: 0.9711 Val PRC: 0.9683 Time: 0.71\n",
      "Epoch: 155 Train Loss: 0.2218 Acc: 0.9133 Pre: 0.9164 Recall: 0.9097 F1: 0.9130 Train AUC: 0.9702 Val AUC: 0.9731 Val PRC: 0.9736 Time: 0.73\n",
      "Epoch: 156 Train Loss: 0.2351 Acc: 0.9118 Pre: 0.9298 Recall: 0.8908 F1: 0.9099 Train AUC: 0.9658 Val AUC: 0.9721 Val PRC: 0.9727 Time: 0.71\n",
      "Epoch: 157 Train Loss: 0.2208 Acc: 0.9128 Pre: 0.9128 Recall: 0.9128 F1: 0.9128 Train AUC: 0.9699 Val AUC: 0.9720 Val PRC: 0.9646 Time: 0.71\n",
      "Epoch: 158 Train Loss: 0.2233 Acc: 0.9034 Pre: 0.8714 Recall: 0.9464 F1: 0.9074 Train AUC: 0.9694 Val AUC: 0.9713 Val PRC: 0.9651 Time: 0.73\n",
      "Epoch: 159 Train Loss: 0.2195 Acc: 0.9107 Pre: 0.9151 Recall: 0.9055 F1: 0.9102 Train AUC: 0.9704 Val AUC: 0.9723 Val PRC: 0.9714 Time: 0.72\n",
      "Epoch: 160 Train Loss: 0.2236 Acc: 0.9123 Pre: 0.9180 Recall: 0.9055 F1: 0.9117 Train AUC: 0.9693 Val AUC: 0.9736 Val PRC: 0.9695 Time: 0.72\n",
      "Epoch: 161 Train Loss: 0.2229 Acc: 0.9133 Pre: 0.9245 Recall: 0.9002 F1: 0.9122 Train AUC: 0.9698 Val AUC: 0.9737 Val PRC: 0.9721 Time: 0.73\n",
      "Epoch: 162 Train Loss: 0.2151 Acc: 0.9175 Pre: 0.9197 Recall: 0.9149 F1: 0.9173 Train AUC: 0.9717 Val AUC: 0.9756 Val PRC: 0.9705 Time: 0.71\n",
      "Epoch: 163 Train Loss: 0.2092 Acc: 0.9170 Pre: 0.9170 Recall: 0.9170 F1: 0.9170 Train AUC: 0.9734 Val AUC: 0.9753 Val PRC: 0.9745 Time: 0.71\n",
      "Epoch: 164 Train Loss: 0.2173 Acc: 0.9160 Pre: 0.9286 Recall: 0.9013 F1: 0.9147 Train AUC: 0.9711 Val AUC: 0.9746 Val PRC: 0.9737 Time: 0.72\n",
      "Epoch: 165 Train Loss: 0.2179 Acc: 0.9149 Pre: 0.9072 Recall: 0.9244 F1: 0.9157 Train AUC: 0.9710 Val AUC: 0.9750 Val PRC: 0.9746 Time: 0.70\n",
      "Epoch: 166 Train Loss: 0.2139 Acc: 0.9207 Pre: 0.9256 Recall: 0.9149 F1: 0.9202 Train AUC: 0.9723 Val AUC: 0.9764 Val PRC: 0.9758 Time: 0.71\n",
      "Epoch: 167 Train Loss: 0.2236 Acc: 0.9202 Pre: 0.9329 Recall: 0.9055 F1: 0.9190 Train AUC: 0.9694 Val AUC: 0.9749 Val PRC: 0.9743 Time: 0.72\n",
      "Epoch: 168 Train Loss: 0.2221 Acc: 0.9160 Pre: 0.9249 Recall: 0.9055 F1: 0.9151 Train AUC: 0.9695 Val AUC: 0.9751 Val PRC: 0.9757 Time: 0.70\n",
      "Epoch: 169 Train Loss: 0.2106 Acc: 0.9144 Pre: 0.8949 Recall: 0.9391 F1: 0.9165 Train AUC: 0.9726 Val AUC: 0.9754 Val PRC: 0.9751 Time: 0.71\n",
      "Epoch: 170 Train Loss: 0.2019 Acc: 0.9175 Pre: 0.9373 Recall: 0.8950 F1: 0.9156 Train AUC: 0.9752 Val AUC: 0.9739 Val PRC: 0.9699 Time: 0.72\n",
      "Epoch: 171 Train Loss: 0.2128 Acc: 0.9196 Pre: 0.9273 Recall: 0.9107 F1: 0.9189 Train AUC: 0.9719 Val AUC: 0.9738 Val PRC: 0.9702 Time: 0.72\n",
      "Epoch: 172 Train Loss: 0.2112 Acc: 0.9160 Pre: 0.9125 Recall: 0.9202 F1: 0.9163 Train AUC: 0.9725 Val AUC: 0.9718 Val PRC: 0.9689 Time: 0.70\n",
      "Epoch: 173 Train Loss: 0.2067 Acc: 0.9186 Pre: 0.9365 Recall: 0.8981 F1: 0.9169 Train AUC: 0.9735 Val AUC: 0.9749 Val PRC: 0.9746 Time: 0.73\n",
      "Epoch: 174 Train Loss: 0.2161 Acc: 0.9181 Pre: 0.9207 Recall: 0.9149 F1: 0.9178 Train AUC: 0.9713 Val AUC: 0.9735 Val PRC: 0.9673 Time: 0.72\n",
      "Epoch: 175 Train Loss: 0.2108 Acc: 0.9186 Pre: 0.9453 Recall: 0.8887 F1: 0.9161 Train AUC: 0.9724 Val AUC: 0.9749 Val PRC: 0.9691 Time: 0.71\n",
      "Epoch: 176 Train Loss: 0.2097 Acc: 0.9196 Pre: 0.9056 Recall: 0.9370 F1: 0.9210 Train AUC: 0.9728 Val AUC: 0.9767 Val PRC: 0.9702 Time: 0.72\n",
      "Epoch: 177 Train Loss: 0.2110 Acc: 0.9202 Pre: 0.9202 Recall: 0.9202 F1: 0.9202 Train AUC: 0.9726 Val AUC: 0.9754 Val PRC: 0.9734 Time: 0.72\n",
      "Epoch: 178 Train Loss: 0.2147 Acc: 0.9186 Pre: 0.9155 Recall: 0.9223 F1: 0.9189 Train AUC: 0.9709 Val AUC: 0.9761 Val PRC: 0.9728 Time: 0.72\n",
      "Epoch: 179 Train Loss: 0.2049 Acc: 0.9170 Pre: 0.8938 Recall: 0.9464 F1: 0.9194 Train AUC: 0.9738 Val AUC: 0.9778 Val PRC: 0.9778 Time: 0.74\n",
      "Epoch: 180 Train Loss: 0.2123 Acc: 0.9149 Pre: 0.9081 Recall: 0.9233 F1: 0.9156 Train AUC: 0.9732 Val AUC: 0.9767 Val PRC: 0.9770 Time: 0.72\n",
      "Epoch: 181 Train Loss: 0.1981 Acc: 0.9217 Pre: 0.9249 Recall: 0.9181 F1: 0.9215 Train AUC: 0.9759 Val AUC: 0.9760 Val PRC: 0.9731 Time: 0.72\n",
      "Epoch: 182 Train Loss: 0.2058 Acc: 0.9217 Pre: 0.9285 Recall: 0.9139 F1: 0.9211 Train AUC: 0.9739 Val AUC: 0.9767 Val PRC: 0.9753 Time: 0.72\n",
      "Epoch: 183 Train Loss: 0.2098 Acc: 0.9191 Pre: 0.8943 Recall: 0.9506 F1: 0.9216 Train AUC: 0.9728 Val AUC: 0.9762 Val PRC: 0.9757 Time: 0.72\n",
      "Epoch: 184 Train Loss: 0.2055 Acc: 0.9144 Pre: 0.9114 Recall: 0.9181 F1: 0.9147 Train AUC: 0.9739 Val AUC: 0.9751 Val PRC: 0.9723 Time: 0.71\n",
      "Epoch: 185 Train Loss: 0.1918 Acc: 0.9228 Pre: 0.9448 Recall: 0.8981 F1: 0.9208 Train AUC: 0.9776 Val AUC: 0.9762 Val PRC: 0.9695 Time: 0.72\n",
      "Epoch: 186 Train Loss: 0.2000 Acc: 0.9160 Pre: 0.9108 Recall: 0.9223 F1: 0.9165 Train AUC: 0.9749 Val AUC: 0.9748 Val PRC: 0.9742 Time: 0.71\n",
      "Epoch: 187 Train Loss: 0.2005 Acc: 0.9212 Pre: 0.9275 Recall: 0.9139 F1: 0.9206 Train AUC: 0.9748 Val AUC: 0.9766 Val PRC: 0.9752 Time: 0.72\n",
      "Epoch: 188 Train Loss: 0.1997 Acc: 0.9181 Pre: 0.8941 Recall: 0.9485 F1: 0.9205 Train AUC: 0.9756 Val AUC: 0.9772 Val PRC: 0.9763 Time: 0.73\n",
      "Epoch: 189 Train Loss: 0.2037 Acc: 0.9254 Pre: 0.9470 Recall: 0.9013 F1: 0.9236 Train AUC: 0.9739 Val AUC: 0.9774 Val PRC: 0.9768 Time: 0.93\n",
      "Epoch: 190 Train Loss: 0.2044 Acc: 0.9249 Pre: 0.9460 Recall: 0.9013 F1: 0.9231 Train AUC: 0.9737 Val AUC: 0.9761 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 191 Train Loss: 0.1835 Acc: 0.9249 Pre: 0.9218 Recall: 0.9286 F1: 0.9252 Train AUC: 0.9791 Val AUC: 0.9776 Val PRC: 0.9713 Time: 0.74\n",
      "Epoch: 192 Train Loss: 0.1947 Acc: 0.9249 Pre: 0.9244 Recall: 0.9254 F1: 0.9249 Train AUC: 0.9772 Val AUC: 0.9766 Val PRC: 0.9755 Time: 0.73\n",
      "Epoch: 193 Train Loss: 0.2007 Acc: 0.9254 Pre: 0.9510 Recall: 0.8971 F1: 0.9232 Train AUC: 0.9746 Val AUC: 0.9766 Val PRC: 0.9748 Time: 0.72\n",
      "Epoch: 194 Train Loss: 0.1993 Acc: 0.9223 Pre: 0.9214 Recall: 0.9233 F1: 0.9224 Train AUC: 0.9755 Val AUC: 0.9760 Val PRC: 0.9710 Time: 0.73\n",
      "Epoch: 195 Train Loss: 0.2016 Acc: 0.9223 Pre: 0.9351 Recall: 0.9076 F1: 0.9211 Train AUC: 0.9747 Val AUC: 0.9764 Val PRC: 0.9732 Time: 0.71\n",
      "Epoch: 196 Train Loss: 0.1982 Acc: 0.9202 Pre: 0.9329 Recall: 0.9055 F1: 0.9190 Train AUC: 0.9761 Val AUC: 0.9766 Val PRC: 0.9770 Time: 0.70\n",
      "Epoch: 197 Train Loss: 0.1958 Acc: 0.9217 Pre: 0.9110 Recall: 0.9349 F1: 0.9228 Train AUC: 0.9761 Val AUC: 0.9775 Val PRC: 0.9772 Time: 0.71\n",
      "Epoch: 198 Train Loss: 0.1948 Acc: 0.9275 Pre: 0.9453 Recall: 0.9076 F1: 0.9260 Train AUC: 0.9768 Val AUC: 0.9781 Val PRC: 0.9766 Time: 0.70\n",
      "Epoch: 199 Train Loss: 0.1939 Acc: 0.9244 Pre: 0.9235 Recall: 0.9254 F1: 0.9244 Train AUC: 0.9762 Val AUC: 0.9782 Val PRC: 0.9768 Time: 0.71\n",
      "Epoch: 200 Train Loss: 0.1960 Acc: 0.9275 Pre: 0.9395 Recall: 0.9139 F1: 0.9265 Train AUC: 0.9751 Val AUC: 0.9784 Val PRC: 0.9747 Time: 0.75\n",
      "Epoch: 201 Train Loss: 0.1926 Acc: 0.9139 Pre: 0.8825 Recall: 0.9548 F1: 0.9173 Train AUC: 0.9785 Val AUC: 0.9770 Val PRC: 0.9763 Time: 0.73\n",
      "Epoch: 202 Train Loss: 0.1930 Acc: 0.9207 Pre: 0.8915 Recall: 0.9580 F1: 0.9235 Train AUC: 0.9769 Val AUC: 0.9793 Val PRC: 0.9758 Time: 0.73\n",
      "Epoch: 203 Train Loss: 0.2002 Acc: 0.9202 Pre: 0.9057 Recall: 0.9380 F1: 0.9216 Train AUC: 0.9752 Val AUC: 0.9767 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 204 Train Loss: 0.1932 Acc: 0.9212 Pre: 0.9186 Recall: 0.9244 F1: 0.9215 Train AUC: 0.9766 Val AUC: 0.9780 Val PRC: 0.9732 Time: 0.72\n",
      "Epoch: 205 Train Loss: 0.2000 Acc: 0.9244 Pre: 0.9335 Recall: 0.9139 F1: 0.9236 Train AUC: 0.9742 Val AUC: 0.9771 Val PRC: 0.9750 Time: 0.71\n",
      "Epoch: 206 Train Loss: 0.2052 Acc: 0.9259 Pre: 0.9051 Recall: 0.9517 F1: 0.9278 Train AUC: 0.9740 Val AUC: 0.9785 Val PRC: 0.9736 Time: 0.71\n",
      "Epoch: 207 Train Loss: 0.1926 Acc: 0.9244 Pre: 0.9114 Recall: 0.9401 F1: 0.9255 Train AUC: 0.9764 Val AUC: 0.9777 Val PRC: 0.9774 Time: 0.74\n",
      "Epoch: 208 Train Loss: 0.1881 Acc: 0.9259 Pre: 0.9300 Recall: 0.9212 F1: 0.9256 Train AUC: 0.9784 Val AUC: 0.9789 Val PRC: 0.9785 Time: 0.74\n",
      "Epoch: 209 Train Loss: 0.1860 Acc: 0.9238 Pre: 0.9080 Recall: 0.9433 F1: 0.9253 Train AUC: 0.9788 Val AUC: 0.9780 Val PRC: 0.9777 Time: 0.72\n",
      "Epoch: 210 Train Loss: 0.1750 Acc: 0.9196 Pre: 0.9157 Recall: 0.9244 F1: 0.9200 Train AUC: 0.9811 Val AUC: 0.9779 Val PRC: 0.9776 Time: 0.73\n",
      "Epoch: 211 Train Loss: 0.1782 Acc: 0.9244 Pre: 0.9156 Recall: 0.9349 F1: 0.9252 Train AUC: 0.9803 Val AUC: 0.9774 Val PRC: 0.9780 Time: 0.72\n",
      "Epoch: 212 Train Loss: 0.1953 Acc: 0.9228 Pre: 0.9111 Recall: 0.9370 F1: 0.9239 Train AUC: 0.9764 Val AUC: 0.9772 Val PRC: 0.9773 Time: 0.71\n",
      "Epoch: 213 Train Loss: 0.1859 Acc: 0.9170 Pre: 0.9093 Recall: 0.9265 F1: 0.9178 Train AUC: 0.9783 Val AUC: 0.9754 Val PRC: 0.9751 Time: 0.73\n",
      "Epoch: 214 Train Loss: 0.1902 Acc: 0.9228 Pre: 0.9154 Recall: 0.9317 F1: 0.9235 Train AUC: 0.9768 Val AUC: 0.9774 Val PRC: 0.9776 Time: 0.71\n",
      "Epoch: 215 Train Loss: 0.1807 Acc: 0.9207 Pre: 0.9256 Recall: 0.9149 F1: 0.9202 Train AUC: 0.9798 Val AUC: 0.9783 Val PRC: 0.9784 Time: 0.73\n",
      "Epoch: 216 Train Loss: 0.1778 Acc: 0.9186 Pre: 0.8873 Recall: 0.9590 F1: 0.9218 Train AUC: 0.9800 Val AUC: 0.9770 Val PRC: 0.9752 Time: 0.72\n",
      "Epoch: 217 Train Loss: 0.1842 Acc: 0.9233 Pre: 0.9549 Recall: 0.8887 F1: 0.9206 Train AUC: 0.9784 Val AUC: 0.9769 Val PRC: 0.9760 Time: 0.71\n",
      "Epoch: 218 Train Loss: 0.1905 Acc: 0.9207 Pre: 0.8993 Recall: 0.9475 F1: 0.9228 Train AUC: 0.9772 Val AUC: 0.9776 Val PRC: 0.9726 Time: 0.70\n",
      "Epoch: 219 Train Loss: 0.1813 Acc: 0.9233 Pre: 0.9251 Recall: 0.9212 F1: 0.9232 Train AUC: 0.9794 Val AUC: 0.9770 Val PRC: 0.9748 Time: 0.72\n",
      "Epoch: 220 Train Loss: 0.1784 Acc: 0.9196 Pre: 0.8991 Recall: 0.9454 F1: 0.9217 Train AUC: 0.9807 Val AUC: 0.9771 Val PRC: 0.9746 Time: 0.71\n",
      "Epoch: 221 Train Loss: 0.1799 Acc: 0.9280 Pre: 0.9137 Recall: 0.9454 F1: 0.9293 Train AUC: 0.9800 Val AUC: 0.9793 Val PRC: 0.9768 Time: 0.71\n",
      "Epoch: 222 Train Loss: 0.1821 Acc: 0.9238 Pre: 0.9391 Recall: 0.9065 F1: 0.9225 Train AUC: 0.9788 Val AUC: 0.9777 Val PRC: 0.9775 Time: 0.72\n",
      "Epoch: 223 Train Loss: 0.1727 Acc: 0.9275 Pre: 0.9302 Recall: 0.9244 F1: 0.9273 Train AUC: 0.9811 Val AUC: 0.9772 Val PRC: 0.9768 Time: 0.70\n",
      "Epoch: 224 Train Loss: 0.1748 Acc: 0.9280 Pre: 0.9249 Recall: 0.9317 F1: 0.9283 Train AUC: 0.9805 Val AUC: 0.9785 Val PRC: 0.9775 Time: 0.71\n",
      "Epoch: 225 Train Loss: 0.1807 Acc: 0.9223 Pre: 0.9044 Recall: 0.9443 F1: 0.9239 Train AUC: 0.9794 Val AUC: 0.9763 Val PRC: 0.9757 Time: 0.74\n",
      "Epoch: 226 Train Loss: 0.1796 Acc: 0.9249 Pre: 0.9090 Recall: 0.9443 F1: 0.9263 Train AUC: 0.9790 Val AUC: 0.9786 Val PRC: 0.9779 Time: 0.71\n",
      "Epoch: 227 Train Loss: 0.1705 Acc: 0.9270 Pre: 0.9178 Recall: 0.9380 F1: 0.9278 Train AUC: 0.9815 Val AUC: 0.9797 Val PRC: 0.9795 Time: 0.71\n",
      "Epoch: 228 Train Loss: 0.1782 Acc: 0.9291 Pre: 0.9277 Recall: 0.9307 F1: 0.9292 Train AUC: 0.9804 Val AUC: 0.9805 Val PRC: 0.9808 Time: 0.73\n",
      "Epoch: 229 Train Loss: 0.1693 Acc: 0.9286 Pre: 0.9304 Recall: 0.9265 F1: 0.9284 Train AUC: 0.9818 Val AUC: 0.9801 Val PRC: 0.9802 Time: 0.73\n",
      "Epoch: 230 Train Loss: 0.1700 Acc: 0.9265 Pre: 0.9452 Recall: 0.9055 F1: 0.9249 Train AUC: 0.9814 Val AUC: 0.9797 Val PRC: 0.9789 Time: 0.72\n",
      "Epoch: 231 Train Loss: 0.1815 Acc: 0.9254 Pre: 0.9108 Recall: 0.9433 F1: 0.9267 Train AUC: 0.9788 Val AUC: 0.9800 Val PRC: 0.9784 Time: 0.74\n",
      "Epoch: 232 Train Loss: 0.1719 Acc: 0.9265 Pre: 0.9220 Recall: 0.9317 F1: 0.9269 Train AUC: 0.9807 Val AUC: 0.9786 Val PRC: 0.9796 Time: 0.72\n",
      "Epoch: 233 Train Loss: 0.1787 Acc: 0.9249 Pre: 0.9317 Recall: 0.9170 F1: 0.9243 Train AUC: 0.9785 Val AUC: 0.9798 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 234 Train Loss: 0.1692 Acc: 0.9249 Pre: 0.9489 Recall: 0.8981 F1: 0.9228 Train AUC: 0.9815 Val AUC: 0.9804 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 235 Train Loss: 0.1681 Acc: 0.9238 Pre: 0.9080 Recall: 0.9433 F1: 0.9253 Train AUC: 0.9816 Val AUC: 0.9801 Val PRC: 0.9745 Time: 0.71\n",
      "Epoch: 236 Train Loss: 0.1754 Acc: 0.9270 Pre: 0.9257 Recall: 0.9286 F1: 0.9271 Train AUC: 0.9803 Val AUC: 0.9784 Val PRC: 0.9780 Time: 0.72\n",
      "Epoch: 237 Train Loss: 0.1706 Acc: 0.9286 Pre: 0.9331 Recall: 0.9233 F1: 0.9282 Train AUC: 0.9806 Val AUC: 0.9790 Val PRC: 0.9750 Time: 0.75\n",
      "Epoch: 238 Train Loss: 0.1630 Acc: 0.9286 Pre: 0.9387 Recall: 0.9170 F1: 0.9277 Train AUC: 0.9828 Val AUC: 0.9779 Val PRC: 0.9764 Time: 0.73\n",
      "Epoch: 239 Train Loss: 0.1657 Acc: 0.9181 Pre: 0.8842 Recall: 0.9622 F1: 0.9215 Train AUC: 0.9822 Val AUC: 0.9795 Val PRC: 0.9797 Time: 0.73\n",
      "Epoch: 240 Train Loss: 0.1666 Acc: 0.9280 Pre: 0.9137 Recall: 0.9454 F1: 0.9293 Train AUC: 0.9817 Val AUC: 0.9795 Val PRC: 0.9753 Time: 0.71\n",
      "Epoch: 241 Train Loss: 0.1670 Acc: 0.9265 Pre: 0.9052 Recall: 0.9527 F1: 0.9284 Train AUC: 0.9819 Val AUC: 0.9794 Val PRC: 0.9791 Time: 0.72\n",
      "Epoch: 242 Train Loss: 0.1604 Acc: 0.9280 Pre: 0.9171 Recall: 0.9412 F1: 0.9290 Train AUC: 0.9834 Val AUC: 0.9807 Val PRC: 0.9802 Time: 0.70\n",
      "Epoch: 243 Train Loss: 0.1678 Acc: 0.9291 Pre: 0.9369 Recall: 0.9202 F1: 0.9285 Train AUC: 0.9814 Val AUC: 0.9805 Val PRC: 0.9801 Time: 0.72\n",
      "Epoch: 244 Train Loss: 0.1659 Acc: 0.9286 Pre: 0.9304 Recall: 0.9265 F1: 0.9284 Train AUC: 0.9818 Val AUC: 0.9802 Val PRC: 0.9807 Time: 0.72\n",
      "Epoch: 245 Train Loss: 0.1627 Acc: 0.9307 Pre: 0.9218 Recall: 0.9412 F1: 0.9314 Train AUC: 0.9827 Val AUC: 0.9810 Val PRC: 0.9813 Time: 0.70\n",
      "Epoch: 246 Train Loss: 0.1560 Acc: 0.9307 Pre: 0.9457 Recall: 0.9139 F1: 0.9295 Train AUC: 0.9839 Val AUC: 0.9810 Val PRC: 0.9798 Time: 0.71\n",
      "Epoch: 247 Train Loss: 0.1609 Acc: 0.9280 Pre: 0.9240 Recall: 0.9328 F1: 0.9284 Train AUC: 0.9831 Val AUC: 0.9807 Val PRC: 0.9800 Time: 0.71\n",
      "Epoch: 248 Train Loss: 0.1596 Acc: 0.9270 Pre: 0.9178 Recall: 0.9380 F1: 0.9278 Train AUC: 0.9831 Val AUC: 0.9802 Val PRC: 0.9810 Time: 0.70\n",
      "Epoch: 249 Train Loss: 0.1511 Acc: 0.9317 Pre: 0.9290 Recall: 0.9349 F1: 0.9319 Train AUC: 0.9855 Val AUC: 0.9816 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 250 Train Loss: 0.1679 Acc: 0.9275 Pre: 0.9162 Recall: 0.9412 F1: 0.9285 Train AUC: 0.9814 Val AUC: 0.9823 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 251 Train Loss: 0.1587 Acc: 0.9265 Pre: 0.9143 Recall: 0.9412 F1: 0.9275 Train AUC: 0.9835 Val AUC: 0.9791 Val PRC: 0.9765 Time: 0.70\n",
      "Epoch: 252 Train Loss: 0.1657 Acc: 0.9265 Pre: 0.9085 Recall: 0.9485 F1: 0.9281 Train AUC: 0.9814 Val AUC: 0.9802 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 253 Train Loss: 0.1551 Acc: 0.9280 Pre: 0.9321 Recall: 0.9233 F1: 0.9277 Train AUC: 0.9840 Val AUC: 0.9807 Val PRC: 0.9793 Time: 0.72\n",
      "Epoch: 254 Train Loss: 0.1586 Acc: 0.9270 Pre: 0.9144 Recall: 0.9422 F1: 0.9281 Train AUC: 0.9833 Val AUC: 0.9784 Val PRC: 0.9784 Time: 0.70\n",
      "Epoch: 255 Train Loss: 0.1513 Acc: 0.9307 Pre: 0.9141 Recall: 0.9506 F1: 0.9320 Train AUC: 0.9856 Val AUC: 0.9809 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 256 Train Loss: 0.1542 Acc: 0.9296 Pre: 0.9208 Recall: 0.9401 F1: 0.9304 Train AUC: 0.9845 Val AUC: 0.9807 Val PRC: 0.9816 Time: 0.71\n",
      "Epoch: 257 Train Loss: 0.1565 Acc: 0.9270 Pre: 0.9230 Recall: 0.9317 F1: 0.9273 Train AUC: 0.9834 Val AUC: 0.9800 Val PRC: 0.9806 Time: 0.72\n",
      "Epoch: 258 Train Loss: 0.1559 Acc: 0.9328 Pre: 0.9402 Recall: 0.9244 F1: 0.9322 Train AUC: 0.9843 Val AUC: 0.9794 Val PRC: 0.9796 Time: 0.72\n",
      "Epoch: 259 Train Loss: 0.1584 Acc: 0.9286 Pre: 0.9155 Recall: 0.9443 F1: 0.9297 Train AUC: 0.9836 Val AUC: 0.9812 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 260 Train Loss: 0.1582 Acc: 0.9259 Pre: 0.9412 Recall: 0.9086 F1: 0.9246 Train AUC: 0.9834 Val AUC: 0.9800 Val PRC: 0.9806 Time: 0.70\n",
      "Epoch: 261 Train Loss: 0.1685 Acc: 0.9265 Pre: 0.8988 Recall: 0.9611 F1: 0.9289 Train AUC: 0.9836 Val AUC: 0.9807 Val PRC: 0.9782 Time: 0.73\n",
      "Epoch: 262 Train Loss: 0.1558 Acc: 0.9291 Pre: 0.9081 Recall: 0.9548 F1: 0.9309 Train AUC: 0.9838 Val AUC: 0.9806 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 263 Train Loss: 0.1656 Acc: 0.9259 Pre: 0.9291 Recall: 0.9223 F1: 0.9257 Train AUC: 0.9813 Val AUC: 0.9800 Val PRC: 0.9793 Time: 0.71\n",
      "Epoch: 264 Train Loss: 0.1623 Acc: 0.9307 Pre: 0.9352 Recall: 0.9254 F1: 0.9303 Train AUC: 0.9821 Val AUC: 0.9810 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 265 Train Loss: 0.1544 Acc: 0.9233 Pre: 0.9014 Recall: 0.9506 F1: 0.9254 Train AUC: 0.9835 Val AUC: 0.9813 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 266 Train Loss: 0.1506 Acc: 0.9280 Pre: 0.9120 Recall: 0.9475 F1: 0.9294 Train AUC: 0.9848 Val AUC: 0.9806 Val PRC: 0.9719 Time: 0.72\n",
      "Epoch: 267 Train Loss: 0.1507 Acc: 0.9301 Pre: 0.9083 Recall: 0.9569 F1: 0.9320 Train AUC: 0.9849 Val AUC: 0.9818 Val PRC: 0.9832 Time: 0.73\n",
      "Epoch: 268 Train Loss: 0.1559 Acc: 0.9291 Pre: 0.9114 Recall: 0.9506 F1: 0.9306 Train AUC: 0.9837 Val AUC: 0.9818 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 269 Train Loss: 0.1482 Acc: 0.9322 Pre: 0.9478 Recall: 0.9149 F1: 0.9311 Train AUC: 0.9856 Val AUC: 0.9810 Val PRC: 0.9816 Time: 0.71\n",
      "Epoch: 270 Train Loss: 0.1508 Acc: 0.9249 Pre: 0.9192 Recall: 0.9317 F1: 0.9254 Train AUC: 0.9849 Val AUC: 0.9792 Val PRC: 0.9768 Time: 0.72\n",
      "Epoch: 271 Train Loss: 0.1523 Acc: 0.9286 Pre: 0.9224 Recall: 0.9359 F1: 0.9291 Train AUC: 0.9842 Val AUC: 0.9801 Val PRC: 0.9805 Time: 0.74\n",
      "Epoch: 272 Train Loss: 0.1646 Acc: 0.9317 Pre: 0.9448 Recall: 0.9170 F1: 0.9307 Train AUC: 0.9817 Val AUC: 0.9807 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 273 Train Loss: 0.1541 Acc: 0.9307 Pre: 0.9545 Recall: 0.9044 F1: 0.9288 Train AUC: 0.9841 Val AUC: 0.9797 Val PRC: 0.9804 Time: 0.72\n",
      "Epoch: 274 Train Loss: 0.1408 Acc: 0.9301 Pre: 0.9217 Recall: 0.9401 F1: 0.9308 Train AUC: 0.9868 Val AUC: 0.9797 Val PRC: 0.9776 Time: 0.72\n",
      "Epoch: 275 Train Loss: 0.1492 Acc: 0.9296 Pre: 0.9426 Recall: 0.9149 F1: 0.9286 Train AUC: 0.9851 Val AUC: 0.9801 Val PRC: 0.9800 Time: 0.71\n",
      "Epoch: 276 Train Loss: 0.1506 Acc: 0.9233 Pre: 0.8982 Recall: 0.9548 F1: 0.9257 Train AUC: 0.9843 Val AUC: 0.9785 Val PRC: 0.9767 Time: 0.71\n",
      "Epoch: 277 Train Loss: 0.1516 Acc: 0.9228 Pre: 0.9062 Recall: 0.9433 F1: 0.9243 Train AUC: 0.9855 Val AUC: 0.9779 Val PRC: 0.9736 Time: 0.71\n",
      "Epoch: 278 Train Loss: 0.1527 Acc: 0.9265 Pre: 0.9134 Recall: 0.9422 F1: 0.9276 Train AUC: 0.9839 Val AUC: 0.9788 Val PRC: 0.9708 Time: 0.72\n",
      "Epoch: 279 Train Loss: 0.1538 Acc: 0.9259 Pre: 0.8964 Recall: 0.9632 F1: 0.9286 Train AUC: 0.9844 Val AUC: 0.9802 Val PRC: 0.9786 Time: 0.71\n",
      "Epoch: 280 Train Loss: 0.1443 Acc: 0.9312 Pre: 0.9228 Recall: 0.9412 F1: 0.9319 Train AUC: 0.9860 Val AUC: 0.9799 Val PRC: 0.9800 Time: 0.72\n",
      "Epoch: 281 Train Loss: 0.1497 Acc: 0.9249 Pre: 0.9192 Recall: 0.9317 F1: 0.9254 Train AUC: 0.9852 Val AUC: 0.9793 Val PRC: 0.9786 Time: 0.74\n",
      "Epoch: 282 Train Loss: 0.1625 Acc: 0.9228 Pre: 0.9070 Recall: 0.9422 F1: 0.9243 Train AUC: 0.9821 Val AUC: 0.9789 Val PRC: 0.9759 Time: 0.73\n",
      "Epoch: 283 Train Loss: 0.1532 Acc: 0.9317 Pre: 0.9281 Recall: 0.9359 F1: 0.9320 Train AUC: 0.9833 Val AUC: 0.9804 Val PRC: 0.9803 Time: 0.73\n",
      "Epoch: 284 Train Loss: 0.1407 Acc: 0.9265 Pre: 0.9151 Recall: 0.9401 F1: 0.9275 Train AUC: 0.9856 Val AUC: 0.9807 Val PRC: 0.9789 Time: 0.73\n",
      "Epoch: 285 Train Loss: 0.1443 Acc: 0.9322 Pre: 0.9517 Recall: 0.9107 F1: 0.9308 Train AUC: 0.9857 Val AUC: 0.9806 Val PRC: 0.9770 Time: 0.73\n",
      "Epoch: 286 Train Loss: 0.1466 Acc: 0.9322 Pre: 0.9195 Recall: 0.9475 F1: 0.9333 Train AUC: 0.9853 Val AUC: 0.9807 Val PRC: 0.9787 Time: 0.75\n",
      "Epoch: 287 Train Loss: 0.1442 Acc: 0.9322 Pre: 0.9282 Recall: 0.9370 F1: 0.9326 Train AUC: 0.9852 Val AUC: 0.9810 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 288 Train Loss: 0.1550 Acc: 0.9317 Pre: 0.9391 Recall: 0.9233 F1: 0.9311 Train AUC: 0.9838 Val AUC: 0.9805 Val PRC: 0.9813 Time: 0.73\n",
      "Epoch: 289 Train Loss: 0.1521 Acc: 0.9249 Pre: 0.9017 Recall: 0.9538 F1: 0.9270 Train AUC: 0.9856 Val AUC: 0.9802 Val PRC: 0.9815 Time: 0.69\n",
      "Epoch: 290 Train Loss: 0.1449 Acc: 0.9317 Pre: 0.9185 Recall: 0.9475 F1: 0.9328 Train AUC: 0.9858 Val AUC: 0.9804 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 291 Train Loss: 0.1536 Acc: 0.9322 Pre: 0.9336 Recall: 0.9307 F1: 0.9321 Train AUC: 0.9836 Val AUC: 0.9802 Val PRC: 0.9805 Time: 0.70\n",
      "Epoch: 292 Train Loss: 0.1460 Acc: 0.9333 Pre: 0.9301 Recall: 0.9370 F1: 0.9335 Train AUC: 0.9861 Val AUC: 0.9818 Val PRC: 0.9831 Time: 0.73\n",
      "Epoch: 293 Train Loss: 0.1424 Acc: 0.9322 Pre: 0.9038 Recall: 0.9674 F1: 0.9346 Train AUC: 0.9870 Val AUC: 0.9816 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 294 Train Loss: 0.1372 Acc: 0.9233 Pre: 0.9096 Recall: 0.9401 F1: 0.9246 Train AUC: 0.9870 Val AUC: 0.9801 Val PRC: 0.9800 Time: 0.71\n",
      "Epoch: 295 Train Loss: 0.1382 Acc: 0.9301 Pre: 0.9183 Recall: 0.9443 F1: 0.9311 Train AUC: 0.9869 Val AUC: 0.9798 Val PRC: 0.9780 Time: 0.71\n",
      "Epoch: 296 Train Loss: 0.1402 Acc: 0.9280 Pre: 0.9249 Recall: 0.9317 F1: 0.9283 Train AUC: 0.9869 Val AUC: 0.9800 Val PRC: 0.9795 Time: 0.74\n",
      "Epoch: 297 Train Loss: 0.1392 Acc: 0.9291 Pre: 0.9198 Recall: 0.9401 F1: 0.9299 Train AUC: 0.9869 Val AUC: 0.9804 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 298 Train Loss: 0.1439 Acc: 0.9291 Pre: 0.9147 Recall: 0.9464 F1: 0.9303 Train AUC: 0.9857 Val AUC: 0.9811 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 299 Train Loss: 0.1324 Acc: 0.9233 Pre: 0.9180 Recall: 0.9296 F1: 0.9238 Train AUC: 0.9883 Val AUC: 0.9793 Val PRC: 0.9801 Time: 0.72\n",
      "Epoch: 300 Train Loss: 0.1488 Acc: 0.9259 Pre: 0.9291 Recall: 0.9223 F1: 0.9257 Train AUC: 0.9848 Val AUC: 0.9797 Val PRC: 0.9796 Time: 0.72\n",
      "Epoch: 301 Train Loss: 0.1393 Acc: 0.9280 Pre: 0.9145 Recall: 0.9443 F1: 0.9292 Train AUC: 0.9863 Val AUC: 0.9797 Val PRC: 0.9795 Time: 0.71\n",
      "Epoch: 302 Train Loss: 0.1326 Acc: 0.9259 Pre: 0.9134 Recall: 0.9412 F1: 0.9271 Train AUC: 0.9882 Val AUC: 0.9798 Val PRC: 0.9802 Time: 0.72\n",
      "Epoch: 303 Train Loss: 0.1477 Acc: 0.9333 Pre: 0.9231 Recall: 0.9454 F1: 0.9341 Train AUC: 0.9845 Val AUC: 0.9802 Val PRC: 0.9811 Time: 0.71\n",
      "Epoch: 304 Train Loss: 0.1437 Acc: 0.9312 Pre: 0.9193 Recall: 0.9454 F1: 0.9322 Train AUC: 0.9858 Val AUC: 0.9803 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 305 Train Loss: 0.1359 Acc: 0.9291 Pre: 0.9173 Recall: 0.9433 F1: 0.9301 Train AUC: 0.9874 Val AUC: 0.9800 Val PRC: 0.9801 Time: 0.72\n",
      "Epoch: 306 Train Loss: 0.1438 Acc: 0.9275 Pre: 0.9330 Recall: 0.9212 F1: 0.9271 Train AUC: 0.9855 Val AUC: 0.9777 Val PRC: 0.9786 Time: 0.72\n",
      "Epoch: 307 Train Loss: 0.1406 Acc: 0.9301 Pre: 0.9261 Recall: 0.9349 F1: 0.9305 Train AUC: 0.9858 Val AUC: 0.9801 Val PRC: 0.9801 Time: 0.71\n",
      "Epoch: 308 Train Loss: 0.1308 Acc: 0.9296 Pre: 0.9208 Recall: 0.9401 F1: 0.9304 Train AUC: 0.9877 Val AUC: 0.9818 Val PRC: 0.9813 Time: 0.70\n",
      "Epoch: 309 Train Loss: 0.1282 Acc: 0.9280 Pre: 0.9171 Recall: 0.9412 F1: 0.9290 Train AUC: 0.9886 Val AUC: 0.9805 Val PRC: 0.9766 Time: 0.71\n",
      "Epoch: 310 Train Loss: 0.1344 Acc: 0.9328 Pre: 0.9392 Recall: 0.9254 F1: 0.9323 Train AUC: 0.9875 Val AUC: 0.9801 Val PRC: 0.9801 Time: 0.70\n",
      "Epoch: 311 Train Loss: 0.1391 Acc: 0.9317 Pre: 0.9587 Recall: 0.9023 F1: 0.9297 Train AUC: 0.9862 Val AUC: 0.9807 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 312 Train Loss: 0.1323 Acc: 0.9296 Pre: 0.9191 Recall: 0.9422 F1: 0.9305 Train AUC: 0.9876 Val AUC: 0.9818 Val PRC: 0.9825 Time: 0.70\n",
      "Epoch: 313 Train Loss: 0.1345 Acc: 0.9280 Pre: 0.9349 Recall: 0.9202 F1: 0.9275 Train AUC: 0.9871 Val AUC: 0.9794 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 314 Train Loss: 0.1289 Acc: 0.9301 Pre: 0.9217 Recall: 0.9401 F1: 0.9308 Train AUC: 0.9884 Val AUC: 0.9800 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 315 Train Loss: 0.1310 Acc: 0.9307 Pre: 0.9236 Recall: 0.9391 F1: 0.9312 Train AUC: 0.9879 Val AUC: 0.9814 Val PRC: 0.9824 Time: 0.70\n",
      "Epoch: 316 Train Loss: 0.1291 Acc: 0.9317 Pre: 0.9354 Recall: 0.9275 F1: 0.9314 Train AUC: 0.9884 Val AUC: 0.9805 Val PRC: 0.9814 Time: 0.70\n",
      "Epoch: 317 Train Loss: 0.1324 Acc: 0.9307 Pre: 0.9125 Recall: 0.9527 F1: 0.9322 Train AUC: 0.9873 Val AUC: 0.9819 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 318 Train Loss: 0.1335 Acc: 0.9301 Pre: 0.9261 Recall: 0.9349 F1: 0.9305 Train AUC: 0.9868 Val AUC: 0.9814 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 319 Train Loss: 0.1278 Acc: 0.9317 Pre: 0.9281 Recall: 0.9359 F1: 0.9320 Train AUC: 0.9888 Val AUC: 0.9812 Val PRC: 0.9820 Time: 0.70\n",
      "Epoch: 320 Train Loss: 0.1285 Acc: 0.9322 Pre: 0.9095 Recall: 0.9601 F1: 0.9341 Train AUC: 0.9882 Val AUC: 0.9811 Val PRC: 0.9808 Time: 0.73\n",
      "Epoch: 321 Train Loss: 0.1427 Acc: 0.9317 Pre: 0.9237 Recall: 0.9412 F1: 0.9324 Train AUC: 0.9879 Val AUC: 0.9820 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 322 Train Loss: 0.1406 Acc: 0.9307 Pre: 0.9051 Recall: 0.9622 F1: 0.9328 Train AUC: 0.9878 Val AUC: 0.9813 Val PRC: 0.9764 Time: 0.91\n",
      "Epoch: 323 Train Loss: 0.1253 Acc: 0.9322 Pre: 0.9247 Recall: 0.9412 F1: 0.9328 Train AUC: 0.9885 Val AUC: 0.9823 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 324 Train Loss: 0.1331 Acc: 0.9291 Pre: 0.9216 Recall: 0.9380 F1: 0.9297 Train AUC: 0.9872 Val AUC: 0.9832 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 325 Train Loss: 0.1194 Acc: 0.9328 Pre: 0.9196 Recall: 0.9485 F1: 0.9338 Train AUC: 0.9894 Val AUC: 0.9805 Val PRC: 0.9801 Time: 0.71\n",
      "Epoch: 326 Train Loss: 0.1269 Acc: 0.9296 Pre: 0.9090 Recall: 0.9548 F1: 0.9314 Train AUC: 0.9884 Val AUC: 0.9819 Val PRC: 0.9828 Time: 0.75\n",
      "Epoch: 327 Train Loss: 0.1232 Acc: 0.9317 Pre: 0.9263 Recall: 0.9380 F1: 0.9322 Train AUC: 0.9892 Val AUC: 0.9810 Val PRC: 0.9816 Time: 0.70\n",
      "Epoch: 328 Train Loss: 0.1314 Acc: 0.9317 Pre: 0.9202 Recall: 0.9454 F1: 0.9326 Train AUC: 0.9872 Val AUC: 0.9815 Val PRC: 0.9809 Time: 0.73\n",
      "Epoch: 329 Train Loss: 0.1254 Acc: 0.9301 Pre: 0.9466 Recall: 0.9118 F1: 0.9288 Train AUC: 0.9892 Val AUC: 0.9796 Val PRC: 0.9808 Time: 0.72\n",
      "Epoch: 330 Train Loss: 0.1178 Acc: 0.9359 Pre: 0.9387 Recall: 0.9328 F1: 0.9357 Train AUC: 0.9897 Val AUC: 0.9815 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 331 Train Loss: 0.1293 Acc: 0.9301 Pre: 0.9343 Recall: 0.9254 F1: 0.9298 Train AUC: 0.9884 Val AUC: 0.9788 Val PRC: 0.9771 Time: 0.72\n",
      "Epoch: 332 Train Loss: 0.1359 Acc: 0.9328 Pre: 0.9355 Recall: 0.9296 F1: 0.9326 Train AUC: 0.9869 Val AUC: 0.9809 Val PRC: 0.9823 Time: 0.71\n",
      "Epoch: 333 Train Loss: 0.1328 Acc: 0.9307 Pre: 0.9556 Recall: 0.9034 F1: 0.9287 Train AUC: 0.9876 Val AUC: 0.9789 Val PRC: 0.9809 Time: 0.71\n",
      "Epoch: 334 Train Loss: 0.1458 Acc: 0.9286 Pre: 0.9232 Recall: 0.9349 F1: 0.9290 Train AUC: 0.9872 Val AUC: 0.9797 Val PRC: 0.9803 Time: 0.72\n",
      "Epoch: 335 Train Loss: 0.1278 Acc: 0.9317 Pre: 0.9220 Recall: 0.9433 F1: 0.9325 Train AUC: 0.9883 Val AUC: 0.9802 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 336 Train Loss: 0.1249 Acc: 0.9338 Pre: 0.9249 Recall: 0.9443 F1: 0.9345 Train AUC: 0.9885 Val AUC: 0.9804 Val PRC: 0.9803 Time: 0.71\n",
      "Epoch: 337 Train Loss: 0.1271 Acc: 0.9380 Pre: 0.9408 Recall: 0.9349 F1: 0.9378 Train AUC: 0.9885 Val AUC: 0.9821 Val PRC: 0.9832 Time: 0.70\n",
      "Epoch: 338 Train Loss: 0.1243 Acc: 0.9312 Pre: 0.9447 Recall: 0.9160 F1: 0.9301 Train AUC: 0.9886 Val AUC: 0.9811 Val PRC: 0.9799 Time: 0.70\n",
      "Epoch: 339 Train Loss: 0.1251 Acc: 0.9343 Pre: 0.9385 Recall: 0.9296 F1: 0.9340 Train AUC: 0.9890 Val AUC: 0.9813 Val PRC: 0.9827 Time: 0.71\n",
      "Epoch: 340 Train Loss: 0.1431 Acc: 0.9238 Pre: 0.9088 Recall: 0.9422 F1: 0.9252 Train AUC: 0.9870 Val AUC: 0.9791 Val PRC: 0.9779 Time: 0.71\n",
      "Epoch: 341 Train Loss: 0.1300 Acc: 0.9312 Pre: 0.9109 Recall: 0.9559 F1: 0.9329 Train AUC: 0.9872 Val AUC: 0.9811 Val PRC: 0.9805 Time: 0.71\n",
      "Epoch: 342 Train Loss: 0.1274 Acc: 0.9322 Pre: 0.9178 Recall: 0.9496 F1: 0.9334 Train AUC: 0.9884 Val AUC: 0.9806 Val PRC: 0.9809 Time: 0.88\n",
      "Epoch: 343 Train Loss: 0.1121 Acc: 0.9364 Pre: 0.9159 Recall: 0.9611 F1: 0.9380 Train AUC: 0.9908 Val AUC: 0.9823 Val PRC: 0.9802 Time: 0.71\n",
      "Epoch: 344 Train Loss: 0.1148 Acc: 0.9328 Pre: 0.9112 Recall: 0.9590 F1: 0.9345 Train AUC: 0.9901 Val AUC: 0.9801 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 345 Train Loss: 0.1224 Acc: 0.9333 Pre: 0.9179 Recall: 0.9517 F1: 0.9345 Train AUC: 0.9888 Val AUC: 0.9817 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 346 Train Loss: 0.1145 Acc: 0.9338 Pre: 0.9302 Recall: 0.9380 F1: 0.9341 Train AUC: 0.9904 Val AUC: 0.9816 Val PRC: 0.9809 Time: 0.73\n",
      "Epoch: 347 Train Loss: 0.1206 Acc: 0.9317 Pre: 0.9194 Recall: 0.9464 F1: 0.9327 Train AUC: 0.9897 Val AUC: 0.9817 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 348 Train Loss: 0.1314 Acc: 0.9322 Pre: 0.9178 Recall: 0.9496 F1: 0.9334 Train AUC: 0.9873 Val AUC: 0.9827 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 349 Train Loss: 0.1199 Acc: 0.9328 Pre: 0.9256 Recall: 0.9412 F1: 0.9333 Train AUC: 0.9898 Val AUC: 0.9795 Val PRC: 0.9804 Time: 0.73\n",
      "Epoch: 350 Train Loss: 0.1292 Acc: 0.9301 Pre: 0.9324 Recall: 0.9275 F1: 0.9300 Train AUC: 0.9878 Val AUC: 0.9793 Val PRC: 0.9803 Time: 0.71\n",
      "Epoch: 351 Train Loss: 0.1204 Acc: 0.9333 Pre: 0.9450 Recall: 0.9202 F1: 0.9324 Train AUC: 0.9892 Val AUC: 0.9800 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 352 Train Loss: 0.1325 Acc: 0.9317 Pre: 0.9354 Recall: 0.9275 F1: 0.9314 Train AUC: 0.9871 Val AUC: 0.9816 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 353 Train Loss: 0.1227 Acc: 0.9296 Pre: 0.9314 Recall: 0.9275 F1: 0.9295 Train AUC: 0.9890 Val AUC: 0.9803 Val PRC: 0.9811 Time: 0.71\n",
      "Epoch: 354 Train Loss: 0.1246 Acc: 0.9312 Pre: 0.9447 Recall: 0.9160 F1: 0.9301 Train AUC: 0.9879 Val AUC: 0.9807 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 355 Train Loss: 0.1219 Acc: 0.9349 Pre: 0.9349 Recall: 0.9349 F1: 0.9349 Train AUC: 0.9886 Val AUC: 0.9799 Val PRC: 0.9796 Time: 0.74\n",
      "Epoch: 356 Train Loss: 0.1106 Acc: 0.9291 Pre: 0.9269 Recall: 0.9317 F1: 0.9293 Train AUC: 0.9907 Val AUC: 0.9797 Val PRC: 0.9783 Time: 0.73\n",
      "Epoch: 357 Train Loss: 0.1254 Acc: 0.9333 Pre: 0.9239 Recall: 0.9443 F1: 0.9340 Train AUC: 0.9878 Val AUC: 0.9810 Val PRC: 0.9803 Time: 0.73\n",
      "Epoch: 358 Train Loss: 0.1205 Acc: 0.9301 Pre: 0.9466 Recall: 0.9118 F1: 0.9288 Train AUC: 0.9892 Val AUC: 0.9824 Val PRC: 0.9831 Time: 0.71\n",
      "Epoch: 359 Train Loss: 0.1202 Acc: 0.9312 Pre: 0.9317 Recall: 0.9307 F1: 0.9312 Train AUC: 0.9889 Val AUC: 0.9800 Val PRC: 0.9776 Time: 0.73\n",
      "Epoch: 360 Train Loss: 0.1200 Acc: 0.9312 Pre: 0.9335 Recall: 0.9286 F1: 0.9310 Train AUC: 0.9894 Val AUC: 0.9813 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 361 Train Loss: 0.1246 Acc: 0.9322 Pre: 0.9354 Recall: 0.9286 F1: 0.9320 Train AUC: 0.9885 Val AUC: 0.9806 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 362 Train Loss: 0.1196 Acc: 0.9286 Pre: 0.9121 Recall: 0.9485 F1: 0.9300 Train AUC: 0.9894 Val AUC: 0.9807 Val PRC: 0.9815 Time: 0.70\n",
      "Epoch: 363 Train Loss: 0.1165 Acc: 0.9312 Pre: 0.9151 Recall: 0.9506 F1: 0.9325 Train AUC: 0.9894 Val AUC: 0.9804 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 364 Train Loss: 0.1173 Acc: 0.9349 Pre: 0.9322 Recall: 0.9380 F1: 0.9351 Train AUC: 0.9893 Val AUC: 0.9807 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 365 Train Loss: 0.1166 Acc: 0.9328 Pre: 0.9292 Recall: 0.9370 F1: 0.9331 Train AUC: 0.9896 Val AUC: 0.9816 Val PRC: 0.9810 Time: 0.70\n",
      "Epoch: 366 Train Loss: 0.1125 Acc: 0.9296 Pre: 0.9379 Recall: 0.9202 F1: 0.9290 Train AUC: 0.9898 Val AUC: 0.9803 Val PRC: 0.9804 Time: 0.72\n",
      "Epoch: 367 Train Loss: 0.1231 Acc: 0.9317 Pre: 0.9363 Recall: 0.9265 F1: 0.9314 Train AUC: 0.9884 Val AUC: 0.9805 Val PRC: 0.9815 Time: 0.71\n",
      "Epoch: 368 Train Loss: 0.1142 Acc: 0.9270 Pre: 0.9135 Recall: 0.9433 F1: 0.9282 Train AUC: 0.9903 Val AUC: 0.9810 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 369 Train Loss: 0.1236 Acc: 0.9307 Pre: 0.9218 Recall: 0.9412 F1: 0.9314 Train AUC: 0.9883 Val AUC: 0.9804 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 370 Train Loss: 0.1081 Acc: 0.9338 Pre: 0.9431 Recall: 0.9233 F1: 0.9331 Train AUC: 0.9912 Val AUC: 0.9811 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 371 Train Loss: 0.1153 Acc: 0.9338 Pre: 0.9384 Recall: 0.9286 F1: 0.9335 Train AUC: 0.9895 Val AUC: 0.9798 Val PRC: 0.9795 Time: 0.71\n",
      "Epoch: 372 Train Loss: 0.1249 Acc: 0.9322 Pre: 0.9458 Recall: 0.9170 F1: 0.9312 Train AUC: 0.9874 Val AUC: 0.9804 Val PRC: 0.9751 Time: 0.72\n",
      "Epoch: 373 Train Loss: 0.1120 Acc: 0.9301 Pre: 0.9297 Recall: 0.9307 F1: 0.9302 Train AUC: 0.9904 Val AUC: 0.9810 Val PRC: 0.9803 Time: 0.71\n",
      "Epoch: 374 Train Loss: 0.1125 Acc: 0.9333 Pre: 0.9328 Recall: 0.9338 F1: 0.9333 Train AUC: 0.9903 Val AUC: 0.9821 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 375 Train Loss: 0.1113 Acc: 0.9317 Pre: 0.9372 Recall: 0.9254 F1: 0.9313 Train AUC: 0.9903 Val AUC: 0.9811 Val PRC: 0.9816 Time: 0.75\n",
      "Epoch: 376 Train Loss: 0.1096 Acc: 0.9301 Pre: 0.9191 Recall: 0.9433 F1: 0.9311 Train AUC: 0.9913 Val AUC: 0.9813 Val PRC: 0.9768 Time: 0.72\n",
      "Epoch: 377 Train Loss: 0.1131 Acc: 0.9338 Pre: 0.9403 Recall: 0.9265 F1: 0.9333 Train AUC: 0.9899 Val AUC: 0.9829 Val PRC: 0.9839 Time: 0.73\n",
      "Epoch: 378 Train Loss: 0.1099 Acc: 0.9322 Pre: 0.9203 Recall: 0.9464 F1: 0.9332 Train AUC: 0.9901 Val AUC: 0.9816 Val PRC: 0.9747 Time: 0.73\n",
      "Epoch: 379 Train Loss: 0.1300 Acc: 0.9354 Pre: 0.9396 Recall: 0.9307 F1: 0.9351 Train AUC: 0.9890 Val AUC: 0.9800 Val PRC: 0.9747 Time: 0.74\n",
      "Epoch: 380 Train Loss: 0.1219 Acc: 0.9333 Pre: 0.9275 Recall: 0.9401 F1: 0.9338 Train AUC: 0.9892 Val AUC: 0.9806 Val PRC: 0.9754 Time: 0.72\n",
      "Epoch: 381 Train Loss: 0.1162 Acc: 0.9328 Pre: 0.9527 Recall: 0.9107 F1: 0.9313 Train AUC: 0.9891 Val AUC: 0.9816 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 382 Train Loss: 0.1247 Acc: 0.9343 Pre: 0.9366 Recall: 0.9317 F1: 0.9342 Train AUC: 0.9906 Val AUC: 0.9826 Val PRC: 0.9818 Time: 0.74\n",
      "Epoch: 383 Train Loss: 0.1284 Acc: 0.9307 Pre: 0.9390 Recall: 0.9212 F1: 0.9300 Train AUC: 0.9873 Val AUC: 0.9811 Val PRC: 0.9813 Time: 0.70\n",
      "Epoch: 384 Train Loss: 0.1169 Acc: 0.9317 Pre: 0.9308 Recall: 0.9328 F1: 0.9318 Train AUC: 0.9899 Val AUC: 0.9815 Val PRC: 0.9828 Time: 0.73\n",
      "Epoch: 385 Train Loss: 0.1270 Acc: 0.9354 Pre: 0.9349 Recall: 0.9359 F1: 0.9354 Train AUC: 0.9878 Val AUC: 0.9812 Val PRC: 0.9826 Time: 0.73\n",
      "Epoch: 386 Train Loss: 0.1138 Acc: 0.9322 Pre: 0.9221 Recall: 0.9443 F1: 0.9331 Train AUC: 0.9905 Val AUC: 0.9793 Val PRC: 0.9790 Time: 0.71\n",
      "Epoch: 387 Train Loss: 0.1105 Acc: 0.9349 Pre: 0.9549 Recall: 0.9128 F1: 0.9334 Train AUC: 0.9909 Val AUC: 0.9806 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 388 Train Loss: 0.1138 Acc: 0.9307 Pre: 0.9192 Recall: 0.9443 F1: 0.9316 Train AUC: 0.9903 Val AUC: 0.9799 Val PRC: 0.9804 Time: 0.72\n",
      "Epoch: 389 Train Loss: 0.1114 Acc: 0.9312 Pre: 0.9335 Recall: 0.9286 F1: 0.9310 Train AUC: 0.9906 Val AUC: 0.9817 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 390 Train Loss: 0.1019 Acc: 0.9312 Pre: 0.9142 Recall: 0.9517 F1: 0.9326 Train AUC: 0.9918 Val AUC: 0.9830 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 391 Train Loss: 0.1169 Acc: 0.9364 Pre: 0.9521 Recall: 0.9191 F1: 0.9353 Train AUC: 0.9897 Val AUC: 0.9840 Val PRC: 0.9851 Time: 0.72\n",
      "Epoch: 392 Train Loss: 0.1156 Acc: 0.9407 Pre: 0.9458 Recall: 0.9349 F1: 0.9403 Train AUC: 0.9895 Val AUC: 0.9832 Val PRC: 0.9847 Time: 0.71\n",
      "Epoch: 393 Train Loss: 0.1078 Acc: 0.9312 Pre: 0.9362 Recall: 0.9254 F1: 0.9308 Train AUC: 0.9913 Val AUC: 0.9817 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 394 Train Loss: 0.1209 Acc: 0.9333 Pre: 0.9356 Recall: 0.9307 F1: 0.9331 Train AUC: 0.9887 Val AUC: 0.9823 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 395 Train Loss: 0.1153 Acc: 0.9307 Pre: 0.9236 Recall: 0.9391 F1: 0.9312 Train AUC: 0.9887 Val AUC: 0.9819 Val PRC: 0.9801 Time: 0.71\n",
      "Epoch: 396 Train Loss: 0.1046 Acc: 0.9380 Pre: 0.9484 Recall: 0.9265 F1: 0.9373 Train AUC: 0.9919 Val AUC: 0.9819 Val PRC: 0.9802 Time: 0.72\n",
      "Epoch: 397 Train Loss: 0.1129 Acc: 0.9364 Pre: 0.9193 Recall: 0.9569 F1: 0.9377 Train AUC: 0.9899 Val AUC: 0.9837 Val PRC: 0.9813 Time: 0.72\n",
      "Epoch: 398 Train Loss: 0.1102 Acc: 0.9322 Pre: 0.9178 Recall: 0.9496 F1: 0.9334 Train AUC: 0.9907 Val AUC: 0.9826 Val PRC: 0.9817 Time: 0.71\n",
      "Epoch: 399 Train Loss: 0.1038 Acc: 0.9307 Pre: 0.9307 Recall: 0.9307 F1: 0.9307 Train AUC: 0.9917 Val AUC: 0.9828 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 400 Train Loss: 0.1120 Acc: 0.9349 Pre: 0.9124 Recall: 0.9622 F1: 0.9366 Train AUC: 0.9902 Val AUC: 0.9822 Val PRC: 0.9832 Time: 0.72\n",
      "Epoch: 401 Train Loss: 0.1053 Acc: 0.9359 Pre: 0.9270 Recall: 0.9464 F1: 0.9366 Train AUC: 0.9917 Val AUC: 0.9823 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 402 Train Loss: 0.1110 Acc: 0.9343 Pre: 0.9164 Recall: 0.9559 F1: 0.9357 Train AUC: 0.9904 Val AUC: 0.9826 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 403 Train Loss: 0.1146 Acc: 0.9307 Pre: 0.9125 Recall: 0.9527 F1: 0.9322 Train AUC: 0.9901 Val AUC: 0.9807 Val PRC: 0.9816 Time: 0.73\n",
      "Epoch: 404 Train Loss: 0.1052 Acc: 0.9375 Pre: 0.9493 Recall: 0.9244 F1: 0.9367 Train AUC: 0.9913 Val AUC: 0.9828 Val PRC: 0.9838 Time: 0.71\n",
      "Epoch: 405 Train Loss: 0.1141 Acc: 0.9338 Pre: 0.9130 Recall: 0.9590 F1: 0.9355 Train AUC: 0.9896 Val AUC: 0.9843 Val PRC: 0.9845 Time: 0.72\n",
      "Epoch: 406 Train Loss: 0.1183 Acc: 0.9333 Pre: 0.9283 Recall: 0.9391 F1: 0.9337 Train AUC: 0.9889 Val AUC: 0.9811 Val PRC: 0.9826 Time: 0.73\n",
      "Epoch: 407 Train Loss: 0.1159 Acc: 0.9338 Pre: 0.9249 Recall: 0.9443 F1: 0.9345 Train AUC: 0.9911 Val AUC: 0.9804 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 408 Train Loss: 0.1033 Acc: 0.9391 Pre: 0.9400 Recall: 0.9380 F1: 0.9390 Train AUC: 0.9917 Val AUC: 0.9816 Val PRC: 0.9819 Time: 0.73\n",
      "Epoch: 409 Train Loss: 0.1222 Acc: 0.9338 Pre: 0.9499 Recall: 0.9160 F1: 0.9326 Train AUC: 0.9903 Val AUC: 0.9823 Val PRC: 0.9836 Time: 0.73\n",
      "Epoch: 410 Train Loss: 0.1068 Acc: 0.9328 Pre: 0.9402 Recall: 0.9244 F1: 0.9322 Train AUC: 0.9909 Val AUC: 0.9802 Val PRC: 0.9813 Time: 0.72\n",
      "Epoch: 411 Train Loss: 0.1139 Acc: 0.9328 Pre: 0.9383 Recall: 0.9265 F1: 0.9323 Train AUC: 0.9899 Val AUC: 0.9794 Val PRC: 0.9801 Time: 0.72\n",
      "Epoch: 412 Train Loss: 0.1066 Acc: 0.9317 Pre: 0.9317 Recall: 0.9317 F1: 0.9317 Train AUC: 0.9914 Val AUC: 0.9793 Val PRC: 0.9803 Time: 0.74\n",
      "Epoch: 413 Train Loss: 0.1096 Acc: 0.9343 Pre: 0.9461 Recall: 0.9212 F1: 0.9335 Train AUC: 0.9901 Val AUC: 0.9808 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 414 Train Loss: 0.1121 Acc: 0.9338 Pre: 0.9357 Recall: 0.9317 F1: 0.9337 Train AUC: 0.9908 Val AUC: 0.9812 Val PRC: 0.9822 Time: 0.72\n",
      "Epoch: 415 Train Loss: 0.1159 Acc: 0.9343 Pre: 0.9189 Recall: 0.9527 F1: 0.9355 Train AUC: 0.9894 Val AUC: 0.9828 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 416 Train Loss: 0.1150 Acc: 0.9338 Pre: 0.9240 Recall: 0.9454 F1: 0.9346 Train AUC: 0.9903 Val AUC: 0.9802 Val PRC: 0.9822 Time: 0.72\n",
      "Epoch: 417 Train Loss: 0.1051 Acc: 0.9386 Pre: 0.9372 Recall: 0.9401 F1: 0.9386 Train AUC: 0.9914 Val AUC: 0.9822 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 418 Train Loss: 0.1052 Acc: 0.9391 Pre: 0.9336 Recall: 0.9454 F1: 0.9395 Train AUC: 0.9913 Val AUC: 0.9823 Val PRC: 0.9827 Time: 0.74\n",
      "Epoch: 419 Train Loss: 0.1130 Acc: 0.9370 Pre: 0.9379 Recall: 0.9359 F1: 0.9369 Train AUC: 0.9895 Val AUC: 0.9818 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 420 Train Loss: 0.1122 Acc: 0.9349 Pre: 0.9199 Recall: 0.9527 F1: 0.9360 Train AUC: 0.9901 Val AUC: 0.9824 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 421 Train Loss: 0.1058 Acc: 0.9338 Pre: 0.9384 Recall: 0.9286 F1: 0.9335 Train AUC: 0.9905 Val AUC: 0.9802 Val PRC: 0.9797 Time: 0.73\n",
      "Epoch: 422 Train Loss: 0.1043 Acc: 0.9291 Pre: 0.9216 Recall: 0.9380 F1: 0.9297 Train AUC: 0.9910 Val AUC: 0.9793 Val PRC: 0.9792 Time: 0.72\n",
      "Epoch: 423 Train Loss: 0.1147 Acc: 0.9343 Pre: 0.9321 Recall: 0.9370 F1: 0.9345 Train AUC: 0.9895 Val AUC: 0.9819 Val PRC: 0.9834 Time: 0.70\n",
      "Epoch: 424 Train Loss: 0.1067 Acc: 0.9364 Pre: 0.9360 Recall: 0.9370 F1: 0.9365 Train AUC: 0.9912 Val AUC: 0.9844 Val PRC: 0.9855 Time: 0.73\n",
      "Epoch: 425 Train Loss: 0.1032 Acc: 0.9343 Pre: 0.9519 Recall: 0.9149 F1: 0.9330 Train AUC: 0.9912 Val AUC: 0.9818 Val PRC: 0.9829 Time: 0.70\n",
      "Epoch: 426 Train Loss: 0.1044 Acc: 0.9364 Pre: 0.9342 Recall: 0.9391 F1: 0.9366 Train AUC: 0.9905 Val AUC: 0.9824 Val PRC: 0.9818 Time: 0.70\n",
      "Epoch: 427 Train Loss: 0.1051 Acc: 0.9380 Pre: 0.9273 Recall: 0.9506 F1: 0.9388 Train AUC: 0.9907 Val AUC: 0.9829 Val PRC: 0.9832 Time: 0.72\n",
      "Epoch: 428 Train Loss: 0.1045 Acc: 0.9343 Pre: 0.9432 Recall: 0.9244 F1: 0.9337 Train AUC: 0.9908 Val AUC: 0.9826 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 429 Train Loss: 0.1107 Acc: 0.9354 Pre: 0.9260 Recall: 0.9464 F1: 0.9361 Train AUC: 0.9895 Val AUC: 0.9834 Val PRC: 0.9836 Time: 0.72\n",
      "Epoch: 430 Train Loss: 0.1012 Acc: 0.9328 Pre: 0.9488 Recall: 0.9149 F1: 0.9316 Train AUC: 0.9923 Val AUC: 0.9820 Val PRC: 0.9830 Time: 0.73\n",
      "Epoch: 431 Train Loss: 0.1119 Acc: 0.9343 Pre: 0.9303 Recall: 0.9391 F1: 0.9347 Train AUC: 0.9900 Val AUC: 0.9831 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 432 Train Loss: 0.1017 Acc: 0.9354 Pre: 0.9286 Recall: 0.9433 F1: 0.9359 Train AUC: 0.9915 Val AUC: 0.9805 Val PRC: 0.9823 Time: 0.71\n",
      "Epoch: 433 Train Loss: 0.1119 Acc: 0.9391 Pre: 0.9593 Recall: 0.9170 F1: 0.9377 Train AUC: 0.9905 Val AUC: 0.9833 Val PRC: 0.9840 Time: 0.71\n",
      "Epoch: 434 Train Loss: 0.1094 Acc: 0.9354 Pre: 0.9396 Recall: 0.9307 F1: 0.9351 Train AUC: 0.9903 Val AUC: 0.9816 Val PRC: 0.9832 Time: 0.71\n",
      "Epoch: 435 Train Loss: 0.1104 Acc: 0.9370 Pre: 0.9426 Recall: 0.9307 F1: 0.9366 Train AUC: 0.9902 Val AUC: 0.9818 Val PRC: 0.9839 Time: 0.71\n",
      "Epoch: 436 Train Loss: 0.0996 Acc: 0.9370 Pre: 0.9407 Recall: 0.9328 F1: 0.9367 Train AUC: 0.9915 Val AUC: 0.9827 Val PRC: 0.9836 Time: 0.72\n",
      "Epoch: 437 Train Loss: 0.1030 Acc: 0.9338 Pre: 0.9206 Recall: 0.9496 F1: 0.9348 Train AUC: 0.9911 Val AUC: 0.9836 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 438 Train Loss: 0.0953 Acc: 0.9349 Pre: 0.9259 Recall: 0.9454 F1: 0.9356 Train AUC: 0.9931 Val AUC: 0.9833 Val PRC: 0.9848 Time: 0.77\n",
      "Epoch: 439 Train Loss: 0.1052 Acc: 0.9317 Pre: 0.9211 Recall: 0.9443 F1: 0.9326 Train AUC: 0.9911 Val AUC: 0.9814 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 440 Train Loss: 0.0964 Acc: 0.9459 Pre: 0.9483 Recall: 0.9433 F1: 0.9458 Train AUC: 0.9925 Val AUC: 0.9857 Val PRC: 0.9864 Time: 0.72\n",
      "Epoch: 441 Train Loss: 0.1084 Acc: 0.9370 Pre: 0.9351 Recall: 0.9391 F1: 0.9371 Train AUC: 0.9900 Val AUC: 0.9819 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 442 Train Loss: 0.0984 Acc: 0.9354 Pre: 0.9414 Recall: 0.9286 F1: 0.9350 Train AUC: 0.9915 Val AUC: 0.9794 Val PRC: 0.9790 Time: 0.72\n",
      "Epoch: 443 Train Loss: 0.1009 Acc: 0.9317 Pre: 0.9354 Recall: 0.9275 F1: 0.9314 Train AUC: 0.9922 Val AUC: 0.9813 Val PRC: 0.9828 Time: 0.73\n",
      "Epoch: 444 Train Loss: 0.0977 Acc: 0.9401 Pre: 0.9604 Recall: 0.9181 F1: 0.9388 Train AUC: 0.9925 Val AUC: 0.9830 Val PRC: 0.9838 Time: 0.73\n",
      "Epoch: 445 Train Loss: 0.1006 Acc: 0.9370 Pre: 0.9426 Recall: 0.9307 F1: 0.9366 Train AUC: 0.9918 Val AUC: 0.9825 Val PRC: 0.9844 Time: 0.71\n",
      "Epoch: 446 Train Loss: 0.0965 Acc: 0.9354 Pre: 0.9386 Recall: 0.9317 F1: 0.9352 Train AUC: 0.9924 Val AUC: 0.9814 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 447 Train Loss: 0.1176 Acc: 0.9343 Pre: 0.9232 Recall: 0.9475 F1: 0.9352 Train AUC: 0.9911 Val AUC: 0.9823 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 448 Train Loss: 0.0957 Acc: 0.9343 Pre: 0.9330 Recall: 0.9359 F1: 0.9345 Train AUC: 0.9922 Val AUC: 0.9828 Val PRC: 0.9829 Time: 0.70\n",
      "Epoch: 449 Train Loss: 0.1139 Acc: 0.9396 Pre: 0.9355 Recall: 0.9443 F1: 0.9399 Train AUC: 0.9912 Val AUC: 0.9835 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 450 Train Loss: 0.0960 Acc: 0.9333 Pre: 0.9319 Recall: 0.9349 F1: 0.9334 Train AUC: 0.9924 Val AUC: 0.9831 Val PRC: 0.9848 Time: 0.70\n",
      "Epoch: 451 Train Loss: 0.0956 Acc: 0.9343 Pre: 0.9376 Recall: 0.9307 F1: 0.9341 Train AUC: 0.9922 Val AUC: 0.9826 Val PRC: 0.9840 Time: 0.70\n",
      "Epoch: 452 Train Loss: 0.0909 Acc: 0.9343 Pre: 0.9321 Recall: 0.9370 F1: 0.9345 Train AUC: 0.9933 Val AUC: 0.9815 Val PRC: 0.9829 Time: 0.72\n",
      "Epoch: 453 Train Loss: 0.1025 Acc: 0.9343 Pre: 0.9285 Recall: 0.9412 F1: 0.9348 Train AUC: 0.9910 Val AUC: 0.9821 Val PRC: 0.9813 Time: 0.72\n",
      "Epoch: 454 Train Loss: 0.0900 Acc: 0.9317 Pre: 0.9290 Recall: 0.9349 F1: 0.9319 Train AUC: 0.9926 Val AUC: 0.9812 Val PRC: 0.9813 Time: 0.71\n",
      "Epoch: 455 Train Loss: 0.0981 Acc: 0.9333 Pre: 0.9239 Recall: 0.9443 F1: 0.9340 Train AUC: 0.9919 Val AUC: 0.9811 Val PRC: 0.9811 Time: 0.91\n",
      "Epoch: 456 Train Loss: 0.0948 Acc: 0.9333 Pre: 0.9205 Recall: 0.9485 F1: 0.9343 Train AUC: 0.9925 Val AUC: 0.9820 Val PRC: 0.9822 Time: 0.70\n",
      "Epoch: 457 Train Loss: 0.0948 Acc: 0.9343 Pre: 0.9470 Recall: 0.9202 F1: 0.9334 Train AUC: 0.9923 Val AUC: 0.9822 Val PRC: 0.9809 Time: 0.71\n",
      "Epoch: 458 Train Loss: 0.0975 Acc: 0.9343 Pre: 0.9285 Recall: 0.9412 F1: 0.9348 Train AUC: 0.9914 Val AUC: 0.9822 Val PRC: 0.9834 Time: 0.73\n",
      "Epoch: 459 Train Loss: 0.0968 Acc: 0.9312 Pre: 0.9193 Recall: 0.9454 F1: 0.9322 Train AUC: 0.9920 Val AUC: 0.9810 Val PRC: 0.9815 Time: 0.73\n",
      "Epoch: 460 Train Loss: 0.0992 Acc: 0.9386 Pre: 0.9475 Recall: 0.9286 F1: 0.9379 Train AUC: 0.9916 Val AUC: 0.9824 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 461 Train Loss: 0.0882 Acc: 0.9359 Pre: 0.9378 Recall: 0.9338 F1: 0.9358 Train AUC: 0.9936 Val AUC: 0.9824 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 462 Train Loss: 0.1001 Acc: 0.9391 Pre: 0.9485 Recall: 0.9286 F1: 0.9384 Train AUC: 0.9919 Val AUC: 0.9838 Val PRC: 0.9838 Time: 0.71\n",
      "Epoch: 463 Train Loss: 0.1064 Acc: 0.9333 Pre: 0.9704 Recall: 0.8939 F1: 0.9306 Train AUC: 0.9924 Val AUC: 0.9818 Val PRC: 0.9821 Time: 0.71\n",
      "Epoch: 464 Train Loss: 0.1048 Acc: 0.9349 Pre: 0.9461 Recall: 0.9223 F1: 0.9340 Train AUC: 0.9908 Val AUC: 0.9826 Val PRC: 0.9833 Time: 0.73\n",
      "Epoch: 465 Train Loss: 0.0972 Acc: 0.9364 Pre: 0.9482 Recall: 0.9233 F1: 0.9356 Train AUC: 0.9923 Val AUC: 0.9823 Val PRC: 0.9838 Time: 0.73\n",
      "Epoch: 466 Train Loss: 0.0961 Acc: 0.9328 Pre: 0.9265 Recall: 0.9401 F1: 0.9333 Train AUC: 0.9924 Val AUC: 0.9827 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 467 Train Loss: 0.0946 Acc: 0.9364 Pre: 0.9397 Recall: 0.9328 F1: 0.9362 Train AUC: 0.9925 Val AUC: 0.9836 Val PRC: 0.9853 Time: 0.76\n",
      "Epoch: 468 Train Loss: 0.0897 Acc: 0.9386 Pre: 0.9475 Recall: 0.9286 F1: 0.9379 Train AUC: 0.9930 Val AUC: 0.9834 Val PRC: 0.9844 Time: 0.70\n",
      "Epoch: 469 Train Loss: 0.0967 Acc: 0.9449 Pre: 0.9608 Recall: 0.9275 F1: 0.9439 Train AUC: 0.9921 Val AUC: 0.9826 Val PRC: 0.9846 Time: 0.71\n",
      "Epoch: 470 Train Loss: 0.0916 Acc: 0.9328 Pre: 0.9256 Recall: 0.9412 F1: 0.9333 Train AUC: 0.9935 Val AUC: 0.9818 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 471 Train Loss: 0.1005 Acc: 0.9380 Pre: 0.9389 Recall: 0.9370 F1: 0.9380 Train AUC: 0.9913 Val AUC: 0.9816 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 472 Train Loss: 0.1071 Acc: 0.9370 Pre: 0.9298 Recall: 0.9454 F1: 0.9375 Train AUC: 0.9921 Val AUC: 0.9825 Val PRC: 0.9826 Time: 0.73\n",
      "Epoch: 473 Train Loss: 0.1113 Acc: 0.9364 Pre: 0.9434 Recall: 0.9286 F1: 0.9359 Train AUC: 0.9920 Val AUC: 0.9820 Val PRC: 0.9839 Time: 0.75\n",
      "Epoch: 474 Train Loss: 0.0893 Acc: 0.9380 Pre: 0.9523 Recall: 0.9223 F1: 0.9370 Train AUC: 0.9932 Val AUC: 0.9820 Val PRC: 0.9808 Time: 0.72\n",
      "Epoch: 475 Train Loss: 0.0920 Acc: 0.9354 Pre: 0.9530 Recall: 0.9160 F1: 0.9341 Train AUC: 0.9928 Val AUC: 0.9832 Val PRC: 0.9846 Time: 0.72\n",
      "Epoch: 476 Train Loss: 0.1090 Acc: 0.9359 Pre: 0.9200 Recall: 0.9548 F1: 0.9371 Train AUC: 0.9912 Val AUC: 0.9822 Val PRC: 0.9803 Time: 0.72\n",
      "Epoch: 477 Train Loss: 0.0935 Acc: 0.9354 Pre: 0.9331 Recall: 0.9380 F1: 0.9356 Train AUC: 0.9920 Val AUC: 0.9826 Val PRC: 0.9832 Time: 0.70\n",
      "Epoch: 478 Train Loss: 0.1033 Acc: 0.9349 Pre: 0.9322 Recall: 0.9380 F1: 0.9351 Train AUC: 0.9913 Val AUC: 0.9813 Val PRC: 0.9831 Time: 0.71\n",
      "Epoch: 479 Train Loss: 0.0879 Acc: 0.9375 Pre: 0.9281 Recall: 0.9485 F1: 0.9382 Train AUC: 0.9940 Val AUC: 0.9815 Val PRC: 0.9828 Time: 0.74\n",
      "Epoch: 480 Train Loss: 0.0908 Acc: 0.9359 Pre: 0.9305 Recall: 0.9422 F1: 0.9363 Train AUC: 0.9926 Val AUC: 0.9813 Val PRC: 0.9834 Time: 0.72\n",
      "Epoch: 481 Train Loss: 0.0953 Acc: 0.9343 Pre: 0.9366 Recall: 0.9317 F1: 0.9342 Train AUC: 0.9920 Val AUC: 0.9827 Val PRC: 0.9839 Time: 0.72\n",
      "Epoch: 482 Train Loss: 0.0919 Acc: 0.9359 Pre: 0.9296 Recall: 0.9433 F1: 0.9364 Train AUC: 0.9924 Val AUC: 0.9837 Val PRC: 0.9847 Time: 0.73\n",
      "Epoch: 483 Train Loss: 0.0969 Acc: 0.9364 Pre: 0.9306 Recall: 0.9433 F1: 0.9369 Train AUC: 0.9924 Val AUC: 0.9834 Val PRC: 0.9851 Time: 0.71\n",
      "Epoch: 484 Train Loss: 0.0964 Acc: 0.9349 Pre: 0.9358 Recall: 0.9338 F1: 0.9348 Train AUC: 0.9924 Val AUC: 0.9812 Val PRC: 0.9821 Time: 0.71\n",
      "Epoch: 485 Train Loss: 0.0898 Acc: 0.9301 Pre: 0.9166 Recall: 0.9464 F1: 0.9313 Train AUC: 0.9929 Val AUC: 0.9818 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 486 Train Loss: 0.0988 Acc: 0.9375 Pre: 0.9325 Recall: 0.9433 F1: 0.9379 Train AUC: 0.9919 Val AUC: 0.9835 Val PRC: 0.9840 Time: 0.72\n",
      "Epoch: 487 Train Loss: 0.0910 Acc: 0.9407 Pre: 0.9575 Recall: 0.9223 F1: 0.9395 Train AUC: 0.9925 Val AUC: 0.9826 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 488 Train Loss: 0.0968 Acc: 0.9428 Pre: 0.9577 Recall: 0.9265 F1: 0.9418 Train AUC: 0.9914 Val AUC: 0.9814 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 489 Train Loss: 0.0962 Acc: 0.9375 Pre: 0.9562 Recall: 0.9170 F1: 0.9362 Train AUC: 0.9921 Val AUC: 0.9819 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 490 Train Loss: 0.0924 Acc: 0.9354 Pre: 0.9295 Recall: 0.9422 F1: 0.9358 Train AUC: 0.9927 Val AUC: 0.9816 Val PRC: 0.9811 Time: 0.71\n",
      "Epoch: 491 Train Loss: 0.0920 Acc: 0.9396 Pre: 0.9401 Recall: 0.9391 F1: 0.9396 Train AUC: 0.9926 Val AUC: 0.9827 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 492 Train Loss: 0.0916 Acc: 0.9380 Pre: 0.9299 Recall: 0.9475 F1: 0.9386 Train AUC: 0.9923 Val AUC: 0.9822 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 493 Train Loss: 0.0950 Acc: 0.9407 Pre: 0.9525 Recall: 0.9275 F1: 0.9399 Train AUC: 0.9914 Val AUC: 0.9831 Val PRC: 0.9848 Time: 0.72\n",
      "Epoch: 494 Train Loss: 0.0939 Acc: 0.9428 Pre: 0.9498 Recall: 0.9349 F1: 0.9423 Train AUC: 0.9927 Val AUC: 0.9833 Val PRC: 0.9846 Time: 0.72\n",
      "Epoch: 495 Train Loss: 0.0941 Acc: 0.9354 Pre: 0.9377 Recall: 0.9328 F1: 0.9352 Train AUC: 0.9918 Val AUC: 0.9805 Val PRC: 0.9803 Time: 0.73\n",
      "Epoch: 496 Train Loss: 0.0942 Acc: 0.9359 Pre: 0.9415 Recall: 0.9296 F1: 0.9355 Train AUC: 0.9914 Val AUC: 0.9807 Val PRC: 0.9797 Time: 0.73\n",
      "Epoch: 497 Train Loss: 0.0885 Acc: 0.9370 Pre: 0.9397 Recall: 0.9338 F1: 0.9368 Train AUC: 0.9928 Val AUC: 0.9818 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 498 Train Loss: 0.0900 Acc: 0.9359 Pre: 0.9560 Recall: 0.9139 F1: 0.9345 Train AUC: 0.9926 Val AUC: 0.9807 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 499 Train Loss: 0.0985 Acc: 0.9364 Pre: 0.9434 Recall: 0.9286 F1: 0.9359 Train AUC: 0.9929 Val AUC: 0.9812 Val PRC: 0.9821 Time: 0.71\n",
      "Epoch: 500 Train Loss: 0.0886 Acc: 0.9322 Pre: 0.9327 Recall: 0.9317 F1: 0.9322 Train AUC: 0.9929 Val AUC: 0.9813 Val PRC: 0.9836 Time: 0.74\n",
      "Fold: 3 Best Epoch: 440 Val acc: 0.9459 Val Pre: 0.9483 Val Recall: 0.9433 Val F1: 0.9458 Val AUC: 0.9857 Val PRC: 0.9864\n",
      "------this is 4th cross validation------\n",
      "total params: 307522\n"
     ]
    },
   
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.7163 Acc: 0.4995 Pre: 0.4997 Recall: 0.9989 F1: 0.6662 Train AUC: 0.5154 Val AUC: 0.5201 Val PRC: 0.5129 Time: 0.76\n",
      "Epoch: 2 Train Loss: 0.6950 Acc: 0.5037 Pre: 0.5018 Recall: 0.9979 F1: 0.6678 Train AUC: 0.5350 Val AUC: 0.5494 Val PRC: 0.5433 Time: 0.74\n",
      "Epoch: 3 Train Loss: 0.6957 Acc: 0.5084 Pre: 0.5043 Recall: 0.9968 F1: 0.6697 Train AUC: 0.5061 Val AUC: 0.5187 Val PRC: 0.5188 Time: 0.74\n",
      "Epoch: 4 Train Loss: 0.7018 Acc: 0.5016 Pre: 0.5008 Recall: 0.9989 F1: 0.6671 Train AUC: 0.5180 Val AUC: 0.5295 Val PRC: 0.5297 Time: 0.71\n",
      "Epoch: 5 Train Loss: 0.6939 Acc: 0.5084 Pre: 0.5043 Recall: 0.9916 F1: 0.6686 Train AUC: 0.5373 Val AUC: 0.5287 Val PRC: 0.5185 Time: 0.72\n",
      "Epoch: 6 Train Loss: 0.6768 Acc: 0.5620 Pre: 0.5345 Recall: 0.9590 F1: 0.6865 Train AUC: 0.6168 Val AUC: 0.6258 Val PRC: 0.6000 Time: 0.71\n",
      "Epoch: 7 Train Loss: 0.6757 Acc: 0.5488 Pre: 0.5266 Recall: 0.9653 F1: 0.6815 Train AUC: 0.6226 Val AUC: 0.6391 Val PRC: 0.6139 Time: 0.71\n",
      "Epoch: 8 Train Loss: 0.6638 Acc: 0.6402 Pre: 0.5892 Recall: 0.9265 F1: 0.7203 Train AUC: 0.6851 Val AUC: 0.6939 Val PRC: 0.6631 Time: 0.72\n",
      "Epoch: 9 Train Loss: 0.6786 Acc: 0.5331 Pre: 0.5173 Recall: 0.9874 F1: 0.6789 Train AUC: 0.6061 Val AUC: 0.6318 Val PRC: 0.6153 Time: 0.71\n",
      "Epoch: 10 Train Loss: 0.6526 Acc: 0.6565 Pre: 0.6096 Recall: 0.8708 F1: 0.7171 Train AUC: 0.7151 Val AUC: 0.7444 Val PRC: 0.7464 Time: 0.73\n",
      "Epoch: 11 Train Loss: 0.6607 Acc: 0.6413 Pre: 0.5982 Recall: 0.8603 F1: 0.7057 Train AUC: 0.6818 Val AUC: 0.6827 Val PRC: 0.6651 Time: 0.71\n",
      "Epoch: 12 Train Loss: 0.6403 Acc: 0.6891 Pre: 0.6424 Recall: 0.8529 F1: 0.7329 Train AUC: 0.7508 Val AUC: 0.7763 Val PRC: 0.7821 Time: 0.72\n",
      "Epoch: 13 Train Loss: 0.6253 Acc: 0.6376 Pre: 0.5886 Recall: 0.9139 F1: 0.7160 Train AUC: 0.7544 Val AUC: 0.7581 Val PRC: 0.7766 Time: 0.73\n",
      "Epoch: 14 Train Loss: 0.6339 Acc: 0.6886 Pre: 0.6517 Recall: 0.8099 F1: 0.7222 Train AUC: 0.7577 Val AUC: 0.7756 Val PRC: 0.7883 Time: 0.72\n",
      "Epoch: 15 Train Loss: 0.6114 Acc: 0.6996 Pre: 0.6527 Recall: 0.8529 F1: 0.7395 Train AUC: 0.7795 Val AUC: 0.7947 Val PRC: 0.8011 Time: 0.72\n",
      "Epoch: 16 Train Loss: 0.6040 Acc: 0.7306 Pre: 0.7077 Recall: 0.7857 F1: 0.7446 Train AUC: 0.7931 Val AUC: 0.8081 Val PRC: 0.8217 Time: 0.75\n",
      "Epoch: 17 Train Loss: 0.5976 Acc: 0.7258 Pre: 0.6983 Recall: 0.7952 F1: 0.7436 Train AUC: 0.8021 Val AUC: 0.8110 Val PRC: 0.8274 Time: 0.74\n",
      "Epoch: 18 Train Loss: 0.5830 Acc: 0.7595 Pre: 0.7380 Recall: 0.8046 F1: 0.7698 Train AUC: 0.8136 Val AUC: 0.8420 Val PRC: 0.8586 Time: 0.72\n",
      "Epoch: 19 Train Loss: 0.5859 Acc: 0.7447 Pre: 0.7349 Recall: 0.7658 F1: 0.7500 Train AUC: 0.8122 Val AUC: 0.8328 Val PRC: 0.8529 Time: 0.77\n",
      "Epoch: 20 Train Loss: 0.5749 Acc: 0.7726 Pre: 0.7537 Recall: 0.8099 F1: 0.7808 Train AUC: 0.8252 Val AUC: 0.8575 Val PRC: 0.8719 Time: 0.73\n",
      "Epoch: 21 Train Loss: 0.5631 Acc: 0.7773 Pre: 0.8000 Recall: 0.7395 F1: 0.7686 Train AUC: 0.8306 Val AUC: 0.8544 Val PRC: 0.8741 Time: 0.73\n",
      "Epoch: 22 Train Loss: 0.5606 Acc: 0.7778 Pre: 0.8191 Recall: 0.7132 F1: 0.7625 Train AUC: 0.8250 Val AUC: 0.8401 Val PRC: 0.8646 Time: 0.73\n",
      "Epoch: 23 Train Loss: 0.5547 Acc: 0.7820 Pre: 0.8118 Recall: 0.7342 F1: 0.7711 Train AUC: 0.8459 Val AUC: 0.8507 Val PRC: 0.8698 Time: 0.73\n",
      "Epoch: 24 Train Loss: 0.5452 Acc: 0.7542 Pre: 0.7300 Recall: 0.8067 F1: 0.7665 Train AUC: 0.8345 Val AUC: 0.8549 Val PRC: 0.8723 Time: 0.72\n",
      "Epoch: 25 Train Loss: 0.5347 Acc: 0.7868 Pre: 0.7961 Recall: 0.7710 F1: 0.7834 Train AUC: 0.8452 Val AUC: 0.8597 Val PRC: 0.8773 Time: 0.72\n",
      "Epoch: 26 Train Loss: 0.5267 Acc: 0.7820 Pre: 0.7960 Recall: 0.7584 F1: 0.7768 Train AUC: 0.8501 Val AUC: 0.8617 Val PRC: 0.8791 Time: 0.71\n",
      "Epoch: 27 Train Loss: 0.5112 Acc: 0.7941 Pre: 0.8182 Recall: 0.7563 F1: 0.7860 Train AUC: 0.8611 Val AUC: 0.8678 Val PRC: 0.8857 Time: 0.72\n",
      "Epoch: 28 Train Loss: 0.5044 Acc: 0.7721 Pre: 0.7606 Recall: 0.7941 F1: 0.7770 Train AUC: 0.8557 Val AUC: 0.8598 Val PRC: 0.8835 Time: 0.71\n",
      "Epoch: 29 Train Loss: 0.5101 Acc: 0.8057 Pre: 0.8464 Recall: 0.7468 F1: 0.7935 Train AUC: 0.8503 Val AUC: 0.8687 Val PRC: 0.8832 Time: 0.71\n",
      "Epoch: 30 Train Loss: 0.5021 Acc: 0.7952 Pre: 0.8306 Recall: 0.7416 F1: 0.7836 Train AUC: 0.8444 Val AUC: 0.8607 Val PRC: 0.8839 Time: 0.72\n",
      "Epoch: 31 Train Loss: 0.4810 Acc: 0.7763 Pre: 0.7630 Recall: 0.8015 F1: 0.7818 Train AUC: 0.8593 Val AUC: 0.8674 Val PRC: 0.8867 Time: 0.72\n",
      "Epoch: 32 Train Loss: 0.4859 Acc: 0.7899 Pre: 0.8013 Recall: 0.7710 F1: 0.7859 Train AUC: 0.8553 Val AUC: 0.8692 Val PRC: 0.8872 Time: 0.72\n",
      "Epoch: 33 Train Loss: 0.4848 Acc: 0.7952 Pre: 0.8452 Recall: 0.7227 F1: 0.7792 Train AUC: 0.8563 Val AUC: 0.8655 Val PRC: 0.8813 Time: 0.74\n",
      "Epoch: 34 Train Loss: 0.4642 Acc: 0.7983 Pre: 0.8373 Recall: 0.7405 F1: 0.7860 Train AUC: 0.8634 Val AUC: 0.8742 Val PRC: 0.8931 Time: 0.72\n",
      "Epoch: 35 Train Loss: 0.4566 Acc: 0.7931 Pre: 0.8033 Recall: 0.7763 F1: 0.7895 Train AUC: 0.8679 Val AUC: 0.8729 Val PRC: 0.8937 Time: 0.72\n",
      "Epoch: 36 Train Loss: 0.4548 Acc: 0.8036 Pre: 0.8440 Recall: 0.7447 F1: 0.7913 Train AUC: 0.8660 Val AUC: 0.8744 Val PRC: 0.8936 Time: 0.74\n",
      "Epoch: 37 Train Loss: 0.4633 Acc: 0.7973 Pre: 0.8306 Recall: 0.7468 F1: 0.7865 Train AUC: 0.8604 Val AUC: 0.8733 Val PRC: 0.8933 Time: 0.73\n",
      "Epoch: 38 Train Loss: 0.4381 Acc: 0.8057 Pre: 0.8456 Recall: 0.7479 F1: 0.7938 Train AUC: 0.8732 Val AUC: 0.8794 Val PRC: 0.8990 Time: 0.71\n",
      "Epoch: 39 Train Loss: 0.4388 Acc: 0.8051 Pre: 0.8521 Recall: 0.7384 F1: 0.7912 Train AUC: 0.8734 Val AUC: 0.8785 Val PRC: 0.8958 Time: 0.71\n",
      "Epoch: 40 Train Loss: 0.4326 Acc: 0.8120 Pre: 0.8722 Recall: 0.7311 F1: 0.7954 Train AUC: 0.8777 Val AUC: 0.8815 Val PRC: 0.9010 Time: 0.70\n",
      "Epoch: 41 Train Loss: 0.4234 Acc: 0.8136 Pre: 0.8491 Recall: 0.7626 F1: 0.8035 Train AUC: 0.8818 Val AUC: 0.8838 Val PRC: 0.9049 Time: 0.71\n",
      "Epoch: 42 Train Loss: 0.4325 Acc: 0.8109 Pre: 0.8844 Recall: 0.7153 F1: 0.7909 Train AUC: 0.8752 Val AUC: 0.8846 Val PRC: 0.9029 Time: 0.71\n",
      "Epoch: 43 Train Loss: 0.4270 Acc: 0.8078 Pre: 0.8447 Recall: 0.7542 F1: 0.7969 Train AUC: 0.8775 Val AUC: 0.8831 Val PRC: 0.9043 Time: 0.74\n",
      "Epoch: 44 Train Loss: 0.4196 Acc: 0.7988 Pre: 0.7882 Recall: 0.8172 F1: 0.8025 Train AUC: 0.8849 Val AUC: 0.8920 Val PRC: 0.9097 Time: 0.71\n",
      "Epoch: 45 Train Loss: 0.4097 Acc: 0.8183 Pre: 0.8428 Recall: 0.7826 F1: 0.8115 Train AUC: 0.8901 Val AUC: 0.8911 Val PRC: 0.9092 Time: 0.71\n",
      "Epoch: 46 Train Loss: 0.4111 Acc: 0.8225 Pre: 0.8603 Recall: 0.7700 F1: 0.8126 Train AUC: 0.8902 Val AUC: 0.8938 Val PRC: 0.9108 Time: 0.71\n",
      "Epoch: 47 Train Loss: 0.4033 Acc: 0.8162 Pre: 0.8265 Recall: 0.8004 F1: 0.8132 Train AUC: 0.8939 Val AUC: 0.8973 Val PRC: 0.9135 Time: 0.72\n",
      "Epoch: 48 Train Loss: 0.4214 Acc: 0.8277 Pre: 0.9031 Recall: 0.7342 F1: 0.8100 Train AUC: 0.8840 Val AUC: 0.8922 Val PRC: 0.9094 Time: 0.75\n",
      "Epoch: 49 Train Loss: 0.3934 Acc: 0.8314 Pre: 0.8479 Recall: 0.8078 F1: 0.8273 Train AUC: 0.8989 Val AUC: 0.9070 Val PRC: 0.9218 Time: 0.74\n",
      "Epoch: 50 Train Loss: 0.3904 Acc: 0.8220 Pre: 0.8560 Recall: 0.7742 F1: 0.8130 Train AUC: 0.9008 Val AUC: 0.9030 Val PRC: 0.9179 Time: 0.73\n",
      "Epoch: 51 Train Loss: 0.3838 Acc: 0.8377 Pre: 0.8796 Recall: 0.7826 F1: 0.8282 Train AUC: 0.9044 Val AUC: 0.9075 Val PRC: 0.9224 Time: 0.73\n",
      "Epoch: 52 Train Loss: 0.3804 Acc: 0.8325 Pre: 0.8467 Recall: 0.8120 F1: 0.8290 Train AUC: 0.9076 Val AUC: 0.9122 Val PRC: 0.9247 Time: 0.74\n",
      "Epoch: 53 Train Loss: 0.3728 Acc: 0.8367 Pre: 0.8688 Recall: 0.7931 F1: 0.8292 Train AUC: 0.9089 Val AUC: 0.9114 Val PRC: 0.9249 Time: 0.72\n",
      "Epoch: 54 Train Loss: 0.3712 Acc: 0.8277 Pre: 0.8257 Recall: 0.8309 F1: 0.8283 Train AUC: 0.9118 Val AUC: 0.9132 Val PRC: 0.9253 Time: 0.71\n",
      "Epoch: 55 Train Loss: 0.3751 Acc: 0.8340 Pre: 0.8464 Recall: 0.8162 F1: 0.8310 Train AUC: 0.9100 Val AUC: 0.9161 Val PRC: 0.9276 Time: 0.71\n",
      "Epoch: 56 Train Loss: 0.3751 Acc: 0.8424 Pre: 0.8844 Recall: 0.7878 F1: 0.8333 Train AUC: 0.9088 Val AUC: 0.9135 Val PRC: 0.9262 Time: 0.72\n",
      "Epoch: 57 Train Loss: 0.3738 Acc: 0.8445 Pre: 0.9110 Recall: 0.7637 F1: 0.8309 Train AUC: 0.9102 Val AUC: 0.9140 Val PRC: 0.9273 Time: 0.72\n",
      "Epoch: 58 Train Loss: 0.3583 Acc: 0.8377 Pre: 0.8560 Recall: 0.8120 F1: 0.8334 Train AUC: 0.9177 Val AUC: 0.9170 Val PRC: 0.9299 Time: 0.71\n",
      "Epoch: 59 Train Loss: 0.3557 Acc: 0.8487 Pre: 0.8843 Recall: 0.8025 F1: 0.8414 Train AUC: 0.9208 Val AUC: 0.9244 Val PRC: 0.9342 Time: 0.72\n",
      "Epoch: 60 Train Loss: 0.3547 Acc: 0.8461 Pre: 0.8809 Recall: 0.8004 F1: 0.8387 Train AUC: 0.9206 Val AUC: 0.9235 Val PRC: 0.9326 Time: 0.72\n",
      "Epoch: 61 Train Loss: 0.3467 Acc: 0.8461 Pre: 0.8509 Recall: 0.8393 F1: 0.8451 Train AUC: 0.9248 Val AUC: 0.9286 Val PRC: 0.9390 Time: 0.72\n",
      "Epoch: 62 Train Loss: 0.3640 Acc: 0.8487 Pre: 0.8722 Recall: 0.8172 F1: 0.8438 Train AUC: 0.9184 Val AUC: 0.9250 Val PRC: 0.9321 Time: 0.73\n",
      "Epoch: 63 Train Loss: 0.3468 Acc: 0.8566 Pre: 0.8751 Recall: 0.8319 F1: 0.8530 Train AUC: 0.9250 Val AUC: 0.9287 Val PRC: 0.9368 Time: 0.71\n",
      "Epoch: 64 Train Loss: 0.3438 Acc: 0.8561 Pre: 0.8599 Recall: 0.8508 F1: 0.8553 Train AUC: 0.9246 Val AUC: 0.9304 Val PRC: 0.9381 Time: 0.73\n",
      "Epoch: 65 Train Loss: 0.3506 Acc: 0.8535 Pre: 0.8516 Recall: 0.8561 F1: 0.8539 Train AUC: 0.9229 Val AUC: 0.9309 Val PRC: 0.9385 Time: 0.72\n",
      "Epoch: 66 Train Loss: 0.3273 Acc: 0.8566 Pre: 0.8570 Recall: 0.8561 F1: 0.8565 Train AUC: 0.9322 Val AUC: 0.9352 Val PRC: 0.9424 Time: 0.71\n",
      "Epoch: 67 Train Loss: 0.3354 Acc: 0.8582 Pre: 0.8866 Recall: 0.8214 F1: 0.8528 Train AUC: 0.9291 Val AUC: 0.9323 Val PRC: 0.9396 Time: 0.72\n",
      "Epoch: 68 Train Loss: 0.3332 Acc: 0.8561 Pre: 0.8653 Recall: 0.8435 F1: 0.8543 Train AUC: 0.9301 Val AUC: 0.9299 Val PRC: 0.9382 Time: 0.72\n",
      "Epoch: 69 Train Loss: 0.3307 Acc: 0.8592 Pre: 0.8869 Recall: 0.8235 F1: 0.8540 Train AUC: 0.9317 Val AUC: 0.9296 Val PRC: 0.9373 Time: 0.70\n",
      "Epoch: 70 Train Loss: 0.3343 Acc: 0.8587 Pre: 0.8749 Recall: 0.8372 F1: 0.8556 Train AUC: 0.9303 Val AUC: 0.9309 Val PRC: 0.9388 Time: 0.72\n",
      "Epoch: 71 Train Loss: 0.3278 Acc: 0.8582 Pre: 0.8515 Recall: 0.8676 F1: 0.8595 Train AUC: 0.9327 Val AUC: 0.9362 Val PRC: 0.9402 Time: 0.72\n",
      "Epoch: 72 Train Loss: 0.3315 Acc: 0.8687 Pre: 0.9120 Recall: 0.8162 F1: 0.8614 Train AUC: 0.9310 Val AUC: 0.9369 Val PRC: 0.9439 Time: 0.74\n",
      "Epoch: 73 Train Loss: 0.3292 Acc: 0.8692 Pre: 0.9064 Recall: 0.8235 F1: 0.8630 Train AUC: 0.9330 Val AUC: 0.9381 Val PRC: 0.9437 Time: 0.71\n",
      "Epoch: 74 Train Loss: 0.3256 Acc: 0.8640 Pre: 0.8898 Recall: 0.8309 F1: 0.8593 Train AUC: 0.9342 Val AUC: 0.9373 Val PRC: 0.9453 Time: 0.71\n",
      "Epoch: 75 Train Loss: 0.3266 Acc: 0.8619 Pre: 0.8600 Recall: 0.8645 F1: 0.8622 Train AUC: 0.9384 Val AUC: 0.9367 Val PRC: 0.9442 Time: 0.73\n",
      "Epoch: 76 Train Loss: 0.3159 Acc: 0.8713 Pre: 0.8872 Recall: 0.8508 F1: 0.8686 Train AUC: 0.9383 Val AUC: 0.9422 Val PRC: 0.9497 Time: 0.72\n",
      "Epoch: 77 Train Loss: 0.3038 Acc: 0.8671 Pre: 0.8967 Recall: 0.8298 F1: 0.8620 Train AUC: 0.9439 Val AUC: 0.9411 Val PRC: 0.9478 Time: 0.72\n",
      "Epoch: 78 Train Loss: 0.3066 Acc: 0.8650 Pre: 0.8639 Recall: 0.8666 F1: 0.8652 Train AUC: 0.9416 Val AUC: 0.9378 Val PRC: 0.9461 Time: 0.74\n",
      "Epoch: 79 Train Loss: 0.3177 Acc: 0.8729 Pre: 0.8842 Recall: 0.8582 F1: 0.8710 Train AUC: 0.9378 Val AUC: 0.9372 Val PRC: 0.9458 Time: 0.71\n",
      "Epoch: 80 Train Loss: 0.3022 Acc: 0.8745 Pre: 0.8896 Recall: 0.8550 F1: 0.8720 Train AUC: 0.9441 Val AUC: 0.9445 Val PRC: 0.9508 Time: 0.74\n",
      "Epoch: 81 Train Loss: 0.3001 Acc: 0.8713 Pre: 0.8757 Recall: 0.8655 F1: 0.8706 Train AUC: 0.9448 Val AUC: 0.9411 Val PRC: 0.9477 Time: 0.71\n",
      "Epoch: 82 Train Loss: 0.3014 Acc: 0.8734 Pre: 0.8810 Recall: 0.8634 F1: 0.8721 Train AUC: 0.9448 Val AUC: 0.9412 Val PRC: 0.9472 Time: 0.72\n",
      "Epoch: 83 Train Loss: 0.2921 Acc: 0.8682 Pre: 0.8693 Recall: 0.8666 F1: 0.8680 Train AUC: 0.9474 Val AUC: 0.9421 Val PRC: 0.9498 Time: 0.72\n",
      "Epoch: 84 Train Loss: 0.2910 Acc: 0.8797 Pre: 0.9021 Recall: 0.8519 F1: 0.8763 Train AUC: 0.9484 Val AUC: 0.9449 Val PRC: 0.9505 Time: 0.71\n",
      "Epoch: 85 Train Loss: 0.2920 Acc: 0.8803 Pre: 0.9200 Recall: 0.8330 F1: 0.8743 Train AUC: 0.9486 Val AUC: 0.9471 Val PRC: 0.9517 Time: 0.93\n",
      "Epoch: 86 Train Loss: 0.3032 Acc: 0.8787 Pre: 0.8823 Recall: 0.8739 F1: 0.8781 Train AUC: 0.9443 Val AUC: 0.9495 Val PRC: 0.9520 Time: 0.72\n",
      "Epoch: 87 Train Loss: 0.2812 Acc: 0.8787 Pre: 0.8831 Recall: 0.8729 F1: 0.8780 Train AUC: 0.9521 Val AUC: 0.9482 Val PRC: 0.9539 Time: 0.70\n",
      "Epoch: 88 Train Loss: 0.2946 Acc: 0.8839 Pre: 0.8795 Recall: 0.8897 F1: 0.8846 Train AUC: 0.9474 Val AUC: 0.9500 Val PRC: 0.9546 Time: 0.71\n",
      "Epoch: 89 Train Loss: 0.2862 Acc: 0.8908 Pre: 0.8867 Recall: 0.8960 F1: 0.8913 Train AUC: 0.9511 Val AUC: 0.9545 Val PRC: 0.9571 Time: 0.72\n",
      "Epoch: 90 Train Loss: 0.2779 Acc: 0.8876 Pre: 0.9165 Recall: 0.8529 F1: 0.8836 Train AUC: 0.9538 Val AUC: 0.9514 Val PRC: 0.9563 Time: 0.71\n",
      "Epoch: 91 Train Loss: 0.2806 Acc: 0.8782 Pre: 0.8711 Recall: 0.8876 F1: 0.8793 Train AUC: 0.9522 Val AUC: 0.9507 Val PRC: 0.9561 Time: 0.71\n",
      "Epoch: 92 Train Loss: 0.2808 Acc: 0.8897 Pre: 0.9122 Recall: 0.8624 F1: 0.8866 Train AUC: 0.9523 Val AUC: 0.9523 Val PRC: 0.9565 Time: 0.73\n",
      "Epoch: 93 Train Loss: 0.2781 Acc: 0.8881 Pre: 0.9012 Recall: 0.8718 F1: 0.8863 Train AUC: 0.9542 Val AUC: 0.9542 Val PRC: 0.9576 Time: 0.71\n",
      "Epoch: 94 Train Loss: 0.2767 Acc: 0.8776 Pre: 0.8964 Recall: 0.8540 F1: 0.8747 Train AUC: 0.9540 Val AUC: 0.9505 Val PRC: 0.9560 Time: 0.71\n",
      "Epoch: 95 Train Loss: 0.2699 Acc: 0.8866 Pre: 0.9098 Recall: 0.8582 F1: 0.8832 Train AUC: 0.9570 Val AUC: 0.9522 Val PRC: 0.9559 Time: 0.72\n",
      "Epoch: 96 Train Loss: 0.2699 Acc: 0.8913 Pre: 0.8984 Recall: 0.8824 F1: 0.8903 Train AUC: 0.9566 Val AUC: 0.9558 Val PRC: 0.9590 Time: 0.71\n",
      "Epoch: 97 Train Loss: 0.2716 Acc: 0.8871 Pre: 0.8967 Recall: 0.8750 F1: 0.8857 Train AUC: 0.9560 Val AUC: 0.9517 Val PRC: 0.9567 Time: 0.73\n",
      "Epoch: 98 Train Loss: 0.2505 Acc: 0.8860 Pre: 0.8777 Recall: 0.8971 F1: 0.8873 Train AUC: 0.9626 Val AUC: 0.9571 Val PRC: 0.9587 Time: 0.72\n",
      "Epoch: 99 Train Loss: 0.2637 Acc: 0.8929 Pre: 0.9165 Recall: 0.8645 F1: 0.8897 Train AUC: 0.9589 Val AUC: 0.9559 Val PRC: 0.9579 Time: 0.71\n",
      "Epoch: 100 Train Loss: 0.2601 Acc: 0.8923 Pre: 0.9155 Recall: 0.8645 F1: 0.8892 Train AUC: 0.9594 Val AUC: 0.9549 Val PRC: 0.9581 Time: 0.72\n",
      "Epoch: 101 Train Loss: 0.2580 Acc: 0.8955 Pre: 0.8984 Recall: 0.8918 F1: 0.8951 Train AUC: 0.9601 Val AUC: 0.9571 Val PRC: 0.9602 Time: 0.73\n",
      "Epoch: 102 Train Loss: 0.2659 Acc: 0.8892 Pre: 0.8746 Recall: 0.9086 F1: 0.8913 Train AUC: 0.9575 Val AUC: 0.9564 Val PRC: 0.9595 Time: 0.71\n",
      "Epoch: 103 Train Loss: 0.2618 Acc: 0.8881 Pre: 0.8790 Recall: 0.9002 F1: 0.8895 Train AUC: 0.9590 Val AUC: 0.9570 Val PRC: 0.9598 Time: 0.70\n",
      "Epoch: 104 Train Loss: 0.2587 Acc: 0.8955 Pre: 0.8976 Recall: 0.8929 F1: 0.8952 Train AUC: 0.9597 Val AUC: 0.9594 Val PRC: 0.9617 Time: 0.71\n",
      "Epoch: 105 Train Loss: 0.2605 Acc: 0.8897 Pre: 0.8825 Recall: 0.8992 F1: 0.8907 Train AUC: 0.9590 Val AUC: 0.9585 Val PRC: 0.9618 Time: 0.71\n",
      "Epoch: 106 Train Loss: 0.2585 Acc: 0.8923 Pre: 0.8895 Recall: 0.8960 F1: 0.8927 Train AUC: 0.9596 Val AUC: 0.9569 Val PRC: 0.9586 Time: 0.73\n",
      "Epoch: 107 Train Loss: 0.2643 Acc: 0.8929 Pre: 0.8824 Recall: 0.9065 F1: 0.8943 Train AUC: 0.9582 Val AUC: 0.9583 Val PRC: 0.9615 Time: 0.73\n",
      "Epoch: 108 Train Loss: 0.2662 Acc: 0.8860 Pre: 0.8679 Recall: 0.9107 F1: 0.8888 Train AUC: 0.9568 Val AUC: 0.9571 Val PRC: 0.9590 Time: 0.71\n",
      "Epoch: 109 Train Loss: 0.2512 Acc: 0.9018 Pre: 0.9153 Recall: 0.8855 F1: 0.9002 Train AUC: 0.9619 Val AUC: 0.9593 Val PRC: 0.9597 Time: 0.72\n",
      "Epoch: 110 Train Loss: 0.2511 Acc: 0.8902 Pre: 0.8826 Recall: 0.9002 F1: 0.8913 Train AUC: 0.9621 Val AUC: 0.9582 Val PRC: 0.9606 Time: 0.70\n",
      "Epoch: 111 Train Loss: 0.2529 Acc: 0.8939 Pre: 0.9076 Recall: 0.8771 F1: 0.8921 Train AUC: 0.9613 Val AUC: 0.9578 Val PRC: 0.9610 Time: 0.73\n",
      "Epoch: 112 Train Loss: 0.2584 Acc: 0.9018 Pre: 0.9022 Recall: 0.9013 F1: 0.9017 Train AUC: 0.9594 Val AUC: 0.9588 Val PRC: 0.9617 Time: 0.71\n",
      "Epoch: 113 Train Loss: 0.2412 Acc: 0.8997 Pre: 0.8903 Recall: 0.9118 F1: 0.9009 Train AUC: 0.9649 Val AUC: 0.9618 Val PRC: 0.9650 Time: 0.72\n",
      "Epoch: 114 Train Loss: 0.2516 Acc: 0.8965 Pre: 0.9218 Recall: 0.8666 F1: 0.8933 Train AUC: 0.9616 Val AUC: 0.9593 Val PRC: 0.9593 Time: 0.73\n",
      "Epoch: 115 Train Loss: 0.2432 Acc: 0.9023 Pre: 0.9118 Recall: 0.8908 F1: 0.9012 Train AUC: 0.9643 Val AUC: 0.9598 Val PRC: 0.9625 Time: 0.72\n",
      "Epoch: 116 Train Loss: 0.2461 Acc: 0.9018 Pre: 0.9048 Recall: 0.8981 F1: 0.9014 Train AUC: 0.9634 Val AUC: 0.9637 Val PRC: 0.9644 Time: 0.72\n",
      "Epoch: 117 Train Loss: 0.2476 Acc: 0.9013 Pre: 0.9161 Recall: 0.8834 F1: 0.8995 Train AUC: 0.9632 Val AUC: 0.9631 Val PRC: 0.9644 Time: 0.72\n",
      "Epoch: 118 Train Loss: 0.2546 Acc: 0.8986 Pre: 0.8999 Recall: 0.8971 F1: 0.8985 Train AUC: 0.9609 Val AUC: 0.9604 Val PRC: 0.9634 Time: 0.70\n",
      "Epoch: 119 Train Loss: 0.2429 Acc: 0.8976 Pre: 0.8930 Recall: 0.9034 F1: 0.8982 Train AUC: 0.9651 Val AUC: 0.9626 Val PRC: 0.9636 Time: 0.71\n",
      "Epoch: 120 Train Loss: 0.2436 Acc: 0.8955 Pre: 0.8926 Recall: 0.8992 F1: 0.8959 Train AUC: 0.9646 Val AUC: 0.9612 Val PRC: 0.9630 Time: 0.72\n",
      "Epoch: 121 Train Loss: 0.2504 Acc: 0.8955 Pre: 0.8830 Recall: 0.9118 F1: 0.8972 Train AUC: 0.9613 Val AUC: 0.9606 Val PRC: 0.9608 Time: 0.72\n",
      "Epoch: 122 Train Loss: 0.2473 Acc: 0.8986 Pre: 0.9240 Recall: 0.8687 F1: 0.8955 Train AUC: 0.9633 Val AUC: 0.9617 Val PRC: 0.9631 Time: 0.70\n",
      "Epoch: 123 Train Loss: 0.2419 Acc: 0.8923 Pre: 0.8879 Recall: 0.8981 F1: 0.8930 Train AUC: 0.9648 Val AUC: 0.9617 Val PRC: 0.9630 Time: 0.72\n",
      "Epoch: 124 Train Loss: 0.2505 Acc: 0.8960 Pre: 0.8762 Recall: 0.9223 F1: 0.8987 Train AUC: 0.9618 Val AUC: 0.9611 Val PRC: 0.9611 Time: 0.71\n",
      "Epoch: 125 Train Loss: 0.2357 Acc: 0.8918 Pre: 0.8837 Recall: 0.9023 F1: 0.8929 Train AUC: 0.9667 Val AUC: 0.9625 Val PRC: 0.9644 Time: 0.71\n",
      "Epoch: 126 Train Loss: 0.2354 Acc: 0.8971 Pre: 0.8810 Recall: 0.9181 F1: 0.8992 Train AUC: 0.9665 Val AUC: 0.9638 Val PRC: 0.9651 Time: 0.72\n",
      "Epoch: 127 Train Loss: 0.2306 Acc: 0.8997 Pre: 0.9131 Recall: 0.8834 F1: 0.8980 Train AUC: 0.9676 Val AUC: 0.9654 Val PRC: 0.9676 Time: 0.71\n",
      "Epoch: 128 Train Loss: 0.2429 Acc: 0.8981 Pre: 0.8981 Recall: 0.8981 F1: 0.8981 Train AUC: 0.9642 Val AUC: 0.9632 Val PRC: 0.9649 Time: 0.71\n",
      "Epoch: 129 Train Loss: 0.2481 Acc: 0.9028 Pre: 0.9155 Recall: 0.8876 F1: 0.9013 Train AUC: 0.9629 Val AUC: 0.9645 Val PRC: 0.9666 Time: 0.72\n",
      "Epoch: 130 Train Loss: 0.2326 Acc: 0.9028 Pre: 0.9155 Recall: 0.8876 F1: 0.9013 Train AUC: 0.9672 Val AUC: 0.9645 Val PRC: 0.9668 Time: 0.71\n",
      "Epoch: 131 Train Loss: 0.2340 Acc: 0.9023 Pre: 0.9172 Recall: 0.8845 F1: 0.9005 Train AUC: 0.9665 Val AUC: 0.9648 Val PRC: 0.9666 Time: 0.73\n",
      "Epoch: 132 Train Loss: 0.2298 Acc: 0.9044 Pre: 0.9131 Recall: 0.8939 F1: 0.9034 Train AUC: 0.9678 Val AUC: 0.9660 Val PRC: 0.9667 Time: 0.72\n",
      "Epoch: 133 Train Loss: 0.2369 Acc: 0.9013 Pre: 0.9072 Recall: 0.8939 F1: 0.9005 Train AUC: 0.9659 Val AUC: 0.9659 Val PRC: 0.9663 Time: 0.72\n",
      "Epoch: 134 Train Loss: 0.2374 Acc: 0.8981 Pre: 0.8964 Recall: 0.9002 F1: 0.8983 Train AUC: 0.9660 Val AUC: 0.9626 Val PRC: 0.9620 Time: 0.71\n",
      "Epoch: 135 Train Loss: 0.2277 Acc: 0.9091 Pre: 0.9323 Recall: 0.8824 F1: 0.9066 Train AUC: 0.9684 Val AUC: 0.9653 Val PRC: 0.9671 Time: 0.73\n",
      "Epoch: 136 Train Loss: 0.2196 Acc: 0.9023 Pre: 0.8998 Recall: 0.9055 F1: 0.9026 Train AUC: 0.9709 Val AUC: 0.9646 Val PRC: 0.9662 Time: 0.73\n",
      "Epoch: 137 Train Loss: 0.2213 Acc: 0.9081 Pre: 0.9209 Recall: 0.8929 F1: 0.9067 Train AUC: 0.9700 Val AUC: 0.9660 Val PRC: 0.9673 Time: 0.72\n",
      "Epoch: 138 Train Loss: 0.2264 Acc: 0.8986 Pre: 0.8869 Recall: 0.9139 F1: 0.9002 Train AUC: 0.9695 Val AUC: 0.9650 Val PRC: 0.9662 Time: 0.71\n",
      "Epoch: 139 Train Loss: 0.2182 Acc: 0.9055 Pre: 0.9242 Recall: 0.8834 F1: 0.9033 Train AUC: 0.9711 Val AUC: 0.9677 Val PRC: 0.9688 Time: 0.71\n",
      "Epoch: 140 Train Loss: 0.2211 Acc: 0.9060 Pre: 0.9206 Recall: 0.8887 F1: 0.9043 Train AUC: 0.9704 Val AUC: 0.9678 Val PRC: 0.9685 Time: 0.71\n",
      "Epoch: 141 Train Loss: 0.2107 Acc: 0.9102 Pre: 0.9268 Recall: 0.8908 F1: 0.9084 Train AUC: 0.9731 Val AUC: 0.9688 Val PRC: 0.9702 Time: 0.71\n",
      "Epoch: 142 Train Loss: 0.2199 Acc: 0.9086 Pre: 0.9247 Recall: 0.8897 F1: 0.9069 Train AUC: 0.9708 Val AUC: 0.9677 Val PRC: 0.9691 Time: 0.71\n",
      "Epoch: 143 Train Loss: 0.2309 Acc: 0.9076 Pre: 0.9154 Recall: 0.8981 F1: 0.9067 Train AUC: 0.9674 Val AUC: 0.9660 Val PRC: 0.9665 Time: 0.71\n",
      "Epoch: 144 Train Loss: 0.2232 Acc: 0.9044 Pre: 0.9395 Recall: 0.8645 F1: 0.9004 Train AUC: 0.9690 Val AUC: 0.9664 Val PRC: 0.9680 Time: 0.70\n",
      "Epoch: 145 Train Loss: 0.2231 Acc: 0.9034 Pre: 0.8810 Recall: 0.9328 F1: 0.9061 Train AUC: 0.9692 Val AUC: 0.9695 Val PRC: 0.9707 Time: 0.72\n",
      "Epoch: 146 Train Loss: 0.2192 Acc: 0.9112 Pre: 0.9279 Recall: 0.8918 F1: 0.9095 Train AUC: 0.9711 Val AUC: 0.9685 Val PRC: 0.9698 Time: 0.70\n",
      "Epoch: 147 Train Loss: 0.2185 Acc: 0.9091 Pre: 0.9220 Recall: 0.8939 F1: 0.9077 Train AUC: 0.9709 Val AUC: 0.9694 Val PRC: 0.9700 Time: 0.70\n",
      "Epoch: 148 Train Loss: 0.2277 Acc: 0.9007 Pre: 0.9234 Recall: 0.8739 F1: 0.8980 Train AUC: 0.9684 Val AUC: 0.9645 Val PRC: 0.9655 Time: 0.72\n",
      "Epoch: 149 Train Loss: 0.2137 Acc: 0.9107 Pre: 0.9222 Recall: 0.8971 F1: 0.9095 Train AUC: 0.9724 Val AUC: 0.9676 Val PRC: 0.9685 Time: 0.71\n",
      "Epoch: 150 Train Loss: 0.2209 Acc: 0.9065 Pre: 0.9152 Recall: 0.8960 F1: 0.9055 Train AUC: 0.9695 Val AUC: 0.9675 Val PRC: 0.9693 Time: 0.70\n",
      "Epoch: 151 Train Loss: 0.2125 Acc: 0.9102 Pre: 0.9185 Recall: 0.9002 F1: 0.9093 Train AUC: 0.9723 Val AUC: 0.9697 Val PRC: 0.9713 Time: 0.72\n",
      "Epoch: 152 Train Loss: 0.2157 Acc: 0.9112 Pre: 0.9099 Recall: 0.9128 F1: 0.9114 Train AUC: 0.9714 Val AUC: 0.9689 Val PRC: 0.9701 Time: 0.70\n",
      "Epoch: 153 Train Loss: 0.2261 Acc: 0.9049 Pre: 0.9141 Recall: 0.8939 F1: 0.9039 Train AUC: 0.9687 Val AUC: 0.9690 Val PRC: 0.9700 Time: 0.71\n",
      "Epoch: 154 Train Loss: 0.2205 Acc: 0.9028 Pre: 0.8893 Recall: 0.9202 F1: 0.9045 Train AUC: 0.9700 Val AUC: 0.9672 Val PRC: 0.9671 Time: 0.73\n",
      "Epoch: 155 Train Loss: 0.2175 Acc: 0.9081 Pre: 0.9265 Recall: 0.8866 F1: 0.9061 Train AUC: 0.9708 Val AUC: 0.9669 Val PRC: 0.9669 Time: 0.72\n",
      "Epoch: 156 Train Loss: 0.2165 Acc: 0.9076 Pre: 0.9000 Recall: 0.9170 F1: 0.9084 Train AUC: 0.9710 Val AUC: 0.9677 Val PRC: 0.9671 Time: 0.71\n",
      "Epoch: 157 Train Loss: 0.2097 Acc: 0.9149 Pre: 0.9266 Recall: 0.9013 F1: 0.9137 Train AUC: 0.9728 Val AUC: 0.9712 Val PRC: 0.9711 Time: 0.73\n",
      "Epoch: 158 Train Loss: 0.2248 Acc: 0.9091 Pre: 0.9096 Recall: 0.9086 F1: 0.9091 Train AUC: 0.9684 Val AUC: 0.9704 Val PRC: 0.9705 Time: 0.70\n",
      "Epoch: 159 Train Loss: 0.2161 Acc: 0.9112 Pre: 0.9152 Recall: 0.9065 F1: 0.9108 Train AUC: 0.9712 Val AUC: 0.9722 Val PRC: 0.9731 Time: 0.71\n",
      "Epoch: 160 Train Loss: 0.2162 Acc: 0.9128 Pre: 0.9235 Recall: 0.9002 F1: 0.9117 Train AUC: 0.9715 Val AUC: 0.9684 Val PRC: 0.9689 Time: 0.73\n",
      "Epoch: 161 Train Loss: 0.2099 Acc: 0.9165 Pre: 0.9169 Recall: 0.9160 F1: 0.9164 Train AUC: 0.9725 Val AUC: 0.9700 Val PRC: 0.9703 Time: 0.72\n",
      "Epoch: 162 Train Loss: 0.2117 Acc: 0.9076 Pre: 0.9181 Recall: 0.8950 F1: 0.9064 Train AUC: 0.9737 Val AUC: 0.9673 Val PRC: 0.9684 Time: 0.71\n",
      "Epoch: 163 Train Loss: 0.2179 Acc: 0.9039 Pre: 0.9026 Recall: 0.9055 F1: 0.9040 Train AUC: 0.9708 Val AUC: 0.9663 Val PRC: 0.9678 Time: 0.72\n",
      "Epoch: 164 Train Loss: 0.2096 Acc: 0.9133 Pre: 0.9367 Recall: 0.8866 F1: 0.9110 Train AUC: 0.9731 Val AUC: 0.9704 Val PRC: 0.9719 Time: 0.72\n",
      "Epoch: 165 Train Loss: 0.2078 Acc: 0.9144 Pre: 0.9350 Recall: 0.8908 F1: 0.9123 Train AUC: 0.9737 Val AUC: 0.9712 Val PRC: 0.9725 Time: 0.70\n",
      "Epoch: 166 Train Loss: 0.2104 Acc: 0.9170 Pre: 0.9269 Recall: 0.9055 F1: 0.9160 Train AUC: 0.9730 Val AUC: 0.9719 Val PRC: 0.9736 Time: 0.74\n",
      "Epoch: 167 Train Loss: 0.2123 Acc: 0.9144 Pre: 0.9210 Recall: 0.9065 F1: 0.9137 Train AUC: 0.9717 Val AUC: 0.9708 Val PRC: 0.9712 Time: 0.72\n",
      "Epoch: 168 Train Loss: 0.2037 Acc: 0.9097 Pre: 0.9037 Recall: 0.9170 F1: 0.9103 Train AUC: 0.9750 Val AUC: 0.9705 Val PRC: 0.9720 Time: 0.72\n",
      "Epoch: 169 Train Loss: 0.2112 Acc: 0.9165 Pre: 0.9178 Recall: 0.9149 F1: 0.9164 Train AUC: 0.9716 Val AUC: 0.9727 Val PRC: 0.9728 Time: 0.73\n",
      "Epoch: 170 Train Loss: 0.2029 Acc: 0.9144 Pre: 0.9046 Recall: 0.9265 F1: 0.9154 Train AUC: 0.9744 Val AUC: 0.9722 Val PRC: 0.9720 Time: 0.72\n",
      "Epoch: 171 Train Loss: 0.2204 Acc: 0.9118 Pre: 0.9058 Recall: 0.9191 F1: 0.9124 Train AUC: 0.9733 Val AUC: 0.9705 Val PRC: 0.9715 Time: 0.71\n",
      "Epoch: 172 Train Loss: 0.1995 Acc: 0.9128 Pre: 0.9163 Recall: 0.9086 F1: 0.9124 Train AUC: 0.9755 Val AUC: 0.9689 Val PRC: 0.9695 Time: 0.72\n",
      "Epoch: 173 Train Loss: 0.1995 Acc: 0.9102 Pre: 0.9150 Recall: 0.9044 F1: 0.9097 Train AUC: 0.9753 Val AUC: 0.9683 Val PRC: 0.9680 Time: 0.71\n",
      "Epoch: 174 Train Loss: 0.2131 Acc: 0.9107 Pre: 0.9259 Recall: 0.8929 F1: 0.9091 Train AUC: 0.9715 Val AUC: 0.9703 Val PRC: 0.9710 Time: 0.70\n",
      "Epoch: 175 Train Loss: 0.2064 Acc: 0.9023 Pre: 0.8861 Recall: 0.9233 F1: 0.9043 Train AUC: 0.9735 Val AUC: 0.9698 Val PRC: 0.9699 Time: 0.71\n",
      "Epoch: 176 Train Loss: 0.2047 Acc: 0.9112 Pre: 0.9108 Recall: 0.9118 F1: 0.9113 Train AUC: 0.9737 Val AUC: 0.9717 Val PRC: 0.9726 Time: 0.71\n",
      "Epoch: 177 Train Loss: 0.2050 Acc: 0.9139 Pre: 0.9087 Recall: 0.9202 F1: 0.9144 Train AUC: 0.9739 Val AUC: 0.9710 Val PRC: 0.9713 Time: 0.72\n",
      "Epoch: 178 Train Loss: 0.2022 Acc: 0.9060 Pre: 0.8869 Recall: 0.9307 F1: 0.9083 Train AUC: 0.9746 Val AUC: 0.9689 Val PRC: 0.9693 Time: 0.71\n",
      "Epoch: 179 Train Loss: 0.1997 Acc: 0.9128 Pre: 0.9085 Recall: 0.9181 F1: 0.9133 Train AUC: 0.9750 Val AUC: 0.9711 Val PRC: 0.9726 Time: 0.70\n",
      "Epoch: 180 Train Loss: 0.1949 Acc: 0.9118 Pre: 0.9083 Recall: 0.9160 F1: 0.9121 Train AUC: 0.9770 Val AUC: 0.9717 Val PRC: 0.9730 Time: 0.70\n",
      "Epoch: 181 Train Loss: 0.2045 Acc: 0.9154 Pre: 0.9133 Recall: 0.9181 F1: 0.9157 Train AUC: 0.9737 Val AUC: 0.9707 Val PRC: 0.9708 Time: 0.72\n",
      "Epoch: 182 Train Loss: 0.2008 Acc: 0.9128 Pre: 0.9119 Recall: 0.9139 F1: 0.9129 Train AUC: 0.9749 Val AUC: 0.9698 Val PRC: 0.9700 Time: 0.72\n",
      "Epoch: 183 Train Loss: 0.2059 Acc: 0.9118 Pre: 0.9161 Recall: 0.9065 F1: 0.9113 Train AUC: 0.9731 Val AUC: 0.9722 Val PRC: 0.9724 Time: 0.70\n",
      "Epoch: 184 Train Loss: 0.2215 Acc: 0.9102 Pre: 0.9115 Recall: 0.9086 F1: 0.9100 Train AUC: 0.9705 Val AUC: 0.9716 Val PRC: 0.9711 Time: 0.71\n",
      "Epoch: 185 Train Loss: 0.1955 Acc: 0.9160 Pre: 0.9323 Recall: 0.8971 F1: 0.9143 Train AUC: 0.9771 Val AUC: 0.9727 Val PRC: 0.9738 Time: 0.72\n",
      "Epoch: 186 Train Loss: 0.2078 Acc: 0.9112 Pre: 0.9099 Recall: 0.9128 F1: 0.9114 Train AUC: 0.9735 Val AUC: 0.9700 Val PRC: 0.9698 Time: 0.70\n",
      "Epoch: 187 Train Loss: 0.2054 Acc: 0.9144 Pre: 0.9088 Recall: 0.9212 F1: 0.9150 Train AUC: 0.9737 Val AUC: 0.9726 Val PRC: 0.9742 Time: 0.70\n",
      "Epoch: 188 Train Loss: 0.2032 Acc: 0.9112 Pre: 0.9260 Recall: 0.8939 F1: 0.9097 Train AUC: 0.9759 Val AUC: 0.9713 Val PRC: 0.9731 Time: 0.73\n",
      "Epoch: 189 Train Loss: 0.1955 Acc: 0.9160 Pre: 0.9258 Recall: 0.9044 F1: 0.9150 Train AUC: 0.9768 Val AUC: 0.9724 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 190 Train Loss: 0.1957 Acc: 0.9154 Pre: 0.9124 Recall: 0.9191 F1: 0.9158 Train AUC: 0.9763 Val AUC: 0.9721 Val PRC: 0.9736 Time: 0.73\n",
      "Epoch: 191 Train Loss: 0.1908 Acc: 0.9181 Pre: 0.9298 Recall: 0.9044 F1: 0.9169 Train AUC: 0.9776 Val AUC: 0.9711 Val PRC: 0.9725 Time: 0.72\n",
      "Epoch: 192 Train Loss: 0.1829 Acc: 0.9196 Pre: 0.9255 Recall: 0.9128 F1: 0.9191 Train AUC: 0.9795 Val AUC: 0.9728 Val PRC: 0.9745 Time: 0.70\n",
      "Epoch: 193 Train Loss: 0.1898 Acc: 0.9149 Pre: 0.9140 Recall: 0.9160 F1: 0.9150 Train AUC: 0.9783 Val AUC: 0.9723 Val PRC: 0.9746 Time: 0.72\n",
      "Epoch: 194 Train Loss: 0.1899 Acc: 0.9112 Pre: 0.9108 Recall: 0.9118 F1: 0.9113 Train AUC: 0.9776 Val AUC: 0.9717 Val PRC: 0.9730 Time: 0.75\n",
      "Epoch: 195 Train Loss: 0.2022 Acc: 0.9133 Pre: 0.9236 Recall: 0.9013 F1: 0.9123 Train AUC: 0.9753 Val AUC: 0.9719 Val PRC: 0.9726 Time: 0.72\n",
      "Epoch: 196 Train Loss: 0.1888 Acc: 0.9165 Pre: 0.9161 Recall: 0.9170 F1: 0.9165 Train AUC: 0.9779 Val AUC: 0.9725 Val PRC: 0.9731 Time: 0.76\n",
      "Epoch: 197 Train Loss: 0.1869 Acc: 0.9196 Pre: 0.9264 Recall: 0.9118 F1: 0.9190 Train AUC: 0.9780 Val AUC: 0.9721 Val PRC: 0.9730 Time: 0.72\n",
      "Epoch: 198 Train Loss: 0.1924 Acc: 0.9170 Pre: 0.9214 Recall: 0.9118 F1: 0.9166 Train AUC: 0.9771 Val AUC: 0.9733 Val PRC: 0.9749 Time: 0.73\n",
      "Epoch: 199 Train Loss: 0.1938 Acc: 0.9207 Pre: 0.9159 Recall: 0.9265 F1: 0.9211 Train AUC: 0.9766 Val AUC: 0.9737 Val PRC: 0.9740 Time: 0.73\n",
      "Epoch: 200 Train Loss: 0.2038 Acc: 0.9175 Pre: 0.9069 Recall: 0.9307 F1: 0.9186 Train AUC: 0.9770 Val AUC: 0.9737 Val PRC: 0.9748 Time: 0.75\n",
      "Epoch: 201 Train Loss: 0.1909 Acc: 0.9123 Pre: 0.8882 Recall: 0.9433 F1: 0.9149 Train AUC: 0.9771 Val AUC: 0.9729 Val PRC: 0.9733 Time: 0.73\n",
      "Epoch: 202 Train Loss: 0.1993 Acc: 0.9144 Pre: 0.9140 Recall: 0.9149 F1: 0.9144 Train AUC: 0.9752 Val AUC: 0.9733 Val PRC: 0.9744 Time: 0.72\n",
      "Epoch: 203 Train Loss: 0.2030 Acc: 0.9196 Pre: 0.9072 Recall: 0.9349 F1: 0.9208 Train AUC: 0.9750 Val AUC: 0.9766 Val PRC: 0.9770 Time: 0.74\n",
      "Epoch: 204 Train Loss: 0.2016 Acc: 0.9128 Pre: 0.9262 Recall: 0.8971 F1: 0.9114 Train AUC: 0.9749 Val AUC: 0.9705 Val PRC: 0.9692 Time: 0.73\n",
      "Epoch: 205 Train Loss: 0.1951 Acc: 0.9170 Pre: 0.9068 Recall: 0.9296 F1: 0.9180 Train AUC: 0.9765 Val AUC: 0.9725 Val PRC: 0.9735 Time: 0.72\n",
      "Epoch: 206 Train Loss: 0.1958 Acc: 0.9144 Pre: 0.9038 Recall: 0.9275 F1: 0.9155 Train AUC: 0.9770 Val AUC: 0.9731 Val PRC: 0.9738 Time: 0.71\n",
      "Epoch: 207 Train Loss: 0.1966 Acc: 0.9160 Pre: 0.9024 Recall: 0.9328 F1: 0.9174 Train AUC: 0.9757 Val AUC: 0.9735 Val PRC: 0.9739 Time: 0.70\n",
      "Epoch: 208 Train Loss: 0.1873 Acc: 0.9118 Pre: 0.9050 Recall: 0.9202 F1: 0.9125 Train AUC: 0.9790 Val AUC: 0.9730 Val PRC: 0.9749 Time: 0.70\n",
      "Epoch: 209 Train Loss: 0.1891 Acc: 0.9160 Pre: 0.9108 Recall: 0.9223 F1: 0.9165 Train AUC: 0.9777 Val AUC: 0.9743 Val PRC: 0.9752 Time: 0.72\n",
      "Epoch: 210 Train Loss: 0.1829 Acc: 0.9165 Pre: 0.9268 Recall: 0.9044 F1: 0.9155 Train AUC: 0.9787 Val AUC: 0.9735 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 211 Train Loss: 0.1894 Acc: 0.9139 Pre: 0.9283 Recall: 0.8971 F1: 0.9124 Train AUC: 0.9772 Val AUC: 0.9718 Val PRC: 0.9726 Time: 0.70\n",
      "Epoch: 212 Train Loss: 0.1956 Acc: 0.9128 Pre: 0.8970 Recall: 0.9328 F1: 0.9145 Train AUC: 0.9756 Val AUC: 0.9712 Val PRC: 0.9727 Time: 0.71\n",
      "Epoch: 213 Train Loss: 0.1881 Acc: 0.9154 Pre: 0.9107 Recall: 0.9212 F1: 0.9159 Train AUC: 0.9778 Val AUC: 0.9708 Val PRC: 0.9717 Time: 0.71\n",
      "Epoch: 214 Train Loss: 0.1935 Acc: 0.9181 Pre: 0.9154 Recall: 0.9212 F1: 0.9183 Train AUC: 0.9755 Val AUC: 0.9740 Val PRC: 0.9745 Time: 0.71\n",
      "Epoch: 215 Train Loss: 0.1829 Acc: 0.9175 Pre: 0.9027 Recall: 0.9359 F1: 0.9190 Train AUC: 0.9786 Val AUC: 0.9739 Val PRC: 0.9750 Time: 0.71\n",
      "Epoch: 216 Train Loss: 0.1864 Acc: 0.9181 Pre: 0.9095 Recall: 0.9286 F1: 0.9189 Train AUC: 0.9779 Val AUC: 0.9742 Val PRC: 0.9728 Time: 0.72\n",
      "Epoch: 217 Train Loss: 0.1896 Acc: 0.9196 Pre: 0.9148 Recall: 0.9254 F1: 0.9201 Train AUC: 0.9775 Val AUC: 0.9752 Val PRC: 0.9747 Time: 0.74\n",
      "Epoch: 218 Train Loss: 0.1838 Acc: 0.9196 Pre: 0.9245 Recall: 0.9139 F1: 0.9192 Train AUC: 0.9786 Val AUC: 0.9755 Val PRC: 0.9754 Time: 0.93\n",
      "Epoch: 219 Train Loss: 0.1862 Acc: 0.9154 Pre: 0.9082 Recall: 0.9244 F1: 0.9162 Train AUC: 0.9781 Val AUC: 0.9731 Val PRC: 0.9727 Time: 0.74\n",
      "Epoch: 220 Train Loss: 0.1805 Acc: 0.9170 Pre: 0.9315 Recall: 0.9002 F1: 0.9156 Train AUC: 0.9793 Val AUC: 0.9718 Val PRC: 0.9716 Time: 0.72\n",
      "Epoch: 221 Train Loss: 0.1853 Acc: 0.9144 Pre: 0.9071 Recall: 0.9233 F1: 0.9151 Train AUC: 0.9780 Val AUC: 0.9723 Val PRC: 0.9726 Time: 0.72\n",
      "Epoch: 222 Train Loss: 0.1906 Acc: 0.9170 Pre: 0.9084 Recall: 0.9275 F1: 0.9179 Train AUC: 0.9771 Val AUC: 0.9729 Val PRC: 0.9747 Time: 0.71\n",
      "Epoch: 223 Train Loss: 0.1719 Acc: 0.9207 Pre: 0.9194 Recall: 0.9223 F1: 0.9208 Train AUC: 0.9813 Val AUC: 0.9753 Val PRC: 0.9768 Time: 0.72\n",
      "Epoch: 224 Train Loss: 0.1762 Acc: 0.9238 Pre: 0.9190 Recall: 0.9296 F1: 0.9243 Train AUC: 0.9799 Val AUC: 0.9763 Val PRC: 0.9766 Time: 0.74\n",
      "Epoch: 225 Train Loss: 0.1820 Acc: 0.9196 Pre: 0.9310 Recall: 0.9065 F1: 0.9186 Train AUC: 0.9788 Val AUC: 0.9743 Val PRC: 0.9741 Time: 0.72\n",
      "Epoch: 226 Train Loss: 0.1816 Acc: 0.9249 Pre: 0.9209 Recall: 0.9296 F1: 0.9252 Train AUC: 0.9795 Val AUC: 0.9764 Val PRC: 0.9771 Time: 0.71\n",
      "Epoch: 227 Train Loss: 0.1768 Acc: 0.9223 Pre: 0.9277 Recall: 0.9160 F1: 0.9218 Train AUC: 0.9804 Val AUC: 0.9768 Val PRC: 0.9783 Time: 0.73\n",
      "Epoch: 228 Train Loss: 0.1794 Acc: 0.9223 Pre: 0.9360 Recall: 0.9065 F1: 0.9210 Train AUC: 0.9796 Val AUC: 0.9762 Val PRC: 0.9774 Time: 0.71\n",
      "Epoch: 229 Train Loss: 0.1762 Acc: 0.9254 Pre: 0.9281 Recall: 0.9223 F1: 0.9252 Train AUC: 0.9802 Val AUC: 0.9766 Val PRC: 0.9774 Time: 0.72\n",
      "Epoch: 230 Train Loss: 0.1775 Acc: 0.9212 Pre: 0.9230 Recall: 0.9191 F1: 0.9211 Train AUC: 0.9798 Val AUC: 0.9744 Val PRC: 0.9756 Time: 0.73\n",
      "Epoch: 231 Train Loss: 0.1738 Acc: 0.9212 Pre: 0.9109 Recall: 0.9338 F1: 0.9222 Train AUC: 0.9807 Val AUC: 0.9750 Val PRC: 0.9764 Time: 0.72\n",
      "Epoch: 232 Train Loss: 0.1675 Acc: 0.9186 Pre: 0.9121 Recall: 0.9265 F1: 0.9192 Train AUC: 0.9822 Val AUC: 0.9757 Val PRC: 0.9762 Time: 0.71\n",
      "Epoch: 233 Train Loss: 0.1743 Acc: 0.9144 Pre: 0.8941 Recall: 0.9401 F1: 0.9165 Train AUC: 0.9803 Val AUC: 0.9736 Val PRC: 0.9732 Time: 0.71\n",
      "Epoch: 234 Train Loss: 0.1681 Acc: 0.9202 Pre: 0.9124 Recall: 0.9296 F1: 0.9209 Train AUC: 0.9815 Val AUC: 0.9758 Val PRC: 0.9772 Time: 0.70\n",
      "Epoch: 235 Train Loss: 0.1724 Acc: 0.9202 Pre: 0.9115 Recall: 0.9307 F1: 0.9210 Train AUC: 0.9802 Val AUC: 0.9752 Val PRC: 0.9755 Time: 0.72\n",
      "Epoch: 236 Train Loss: 0.1783 Acc: 0.9259 Pre: 0.9300 Recall: 0.9212 F1: 0.9256 Train AUC: 0.9795 Val AUC: 0.9778 Val PRC: 0.9748 Time: 0.72\n",
      "Epoch: 237 Train Loss: 0.1702 Acc: 0.9207 Pre: 0.9185 Recall: 0.9233 F1: 0.9209 Train AUC: 0.9813 Val AUC: 0.9774 Val PRC: 0.9788 Time: 0.70\n",
      "Epoch: 238 Train Loss: 0.1681 Acc: 0.9202 Pre: 0.8992 Recall: 0.9464 F1: 0.9222 Train AUC: 0.9821 Val AUC: 0.9757 Val PRC: 0.9753 Time: 0.73\n",
      "Epoch: 239 Train Loss: 0.1811 Acc: 0.9165 Pre: 0.8985 Recall: 0.9391 F1: 0.9183 Train AUC: 0.9793 Val AUC: 0.9753 Val PRC: 0.9749 Time: 0.72\n",
      "Epoch: 240 Train Loss: 0.1680 Acc: 0.9212 Pre: 0.9126 Recall: 0.9317 F1: 0.9220 Train AUC: 0.9818 Val AUC: 0.9748 Val PRC: 0.9753 Time: 0.72\n",
      "Epoch: 241 Train Loss: 0.1748 Acc: 0.9196 Pre: 0.9131 Recall: 0.9275 F1: 0.9203 Train AUC: 0.9806 Val AUC: 0.9755 Val PRC: 0.9774 Time: 0.71\n",
      "Epoch: 242 Train Loss: 0.1667 Acc: 0.9160 Pre: 0.9108 Recall: 0.9223 F1: 0.9165 Train AUC: 0.9824 Val AUC: 0.9731 Val PRC: 0.9746 Time: 0.72\n",
      "Epoch: 243 Train Loss: 0.1662 Acc: 0.9228 Pre: 0.9457 Recall: 0.8971 F1: 0.9208 Train AUC: 0.9824 Val AUC: 0.9744 Val PRC: 0.9769 Time: 0.72\n",
      "Epoch: 244 Train Loss: 0.1662 Acc: 0.9212 Pre: 0.9397 Recall: 0.9002 F1: 0.9195 Train AUC: 0.9823 Val AUC: 0.9747 Val PRC: 0.9754 Time: 0.72\n",
      "Epoch: 245 Train Loss: 0.1623 Acc: 0.9217 Pre: 0.9294 Recall: 0.9128 F1: 0.9210 Train AUC: 0.9836 Val AUC: 0.9772 Val PRC: 0.9780 Time: 0.72\n",
      "Epoch: 246 Train Loss: 0.1619 Acc: 0.9202 Pre: 0.9301 Recall: 0.9086 F1: 0.9192 Train AUC: 0.9834 Val AUC: 0.9755 Val PRC: 0.9771 Time: 0.72\n",
      "Epoch: 247 Train Loss: 0.1617 Acc: 0.9202 Pre: 0.9115 Recall: 0.9307 F1: 0.9210 Train AUC: 0.9831 Val AUC: 0.9768 Val PRC: 0.9759 Time: 0.71\n",
      "Epoch: 248 Train Loss: 0.1709 Acc: 0.9254 Pre: 0.9245 Recall: 0.9265 F1: 0.9255 Train AUC: 0.9808 Val AUC: 0.9775 Val PRC: 0.9773 Time: 0.71\n",
      "Epoch: 249 Train Loss: 0.1663 Acc: 0.9212 Pre: 0.9143 Recall: 0.9296 F1: 0.9219 Train AUC: 0.9820 Val AUC: 0.9775 Val PRC: 0.9772 Time: 0.72\n",
      "Epoch: 250 Train Loss: 0.1763 Acc: 0.9238 Pre: 0.9343 Recall: 0.9118 F1: 0.9229 Train AUC: 0.9795 Val AUC: 0.9773 Val PRC: 0.9772 Time: 0.71\n",
      "Epoch: 251 Train Loss: 0.1651 Acc: 0.9217 Pre: 0.8987 Recall: 0.9506 F1: 0.9239 Train AUC: 0.9825 Val AUC: 0.9784 Val PRC: 0.9786 Time: 0.70\n",
      "Epoch: 252 Train Loss: 0.1678 Acc: 0.9175 Pre: 0.9102 Recall: 0.9265 F1: 0.9183 Train AUC: 0.9815 Val AUC: 0.9762 Val PRC: 0.9778 Time: 0.73\n",
      "Epoch: 253 Train Loss: 0.1663 Acc: 0.9244 Pre: 0.9174 Recall: 0.9328 F1: 0.9250 Train AUC: 0.9818 Val AUC: 0.9760 Val PRC: 0.9780 Time: 0.71\n",
      "Epoch: 254 Train Loss: 0.1643 Acc: 0.9202 Pre: 0.9348 Recall: 0.9034 F1: 0.9188 Train AUC: 0.9821 Val AUC: 0.9766 Val PRC: 0.9787 Time: 0.73\n",
      "Epoch: 255 Train Loss: 0.1721 Acc: 0.9217 Pre: 0.9294 Recall: 0.9128 F1: 0.9210 Train AUC: 0.9802 Val AUC: 0.9758 Val PRC: 0.9781 Time: 0.73\n",
      "Epoch: 256 Train Loss: 0.1601 Acc: 0.9196 Pre: 0.9282 Recall: 0.9097 F1: 0.9188 Train AUC: 0.9830 Val AUC: 0.9750 Val PRC: 0.9765 Time: 0.70\n",
      "Epoch: 257 Train Loss: 0.1646 Acc: 0.9265 Pre: 0.9310 Recall: 0.9212 F1: 0.9261 Train AUC: 0.9820 Val AUC: 0.9746 Val PRC: 0.9765 Time: 0.71\n",
      "Epoch: 258 Train Loss: 0.1688 Acc: 0.9212 Pre: 0.9160 Recall: 0.9275 F1: 0.9217 Train AUC: 0.9811 Val AUC: 0.9759 Val PRC: 0.9772 Time: 0.73\n",
      "Epoch: 259 Train Loss: 0.1657 Acc: 0.9244 Pre: 0.9165 Recall: 0.9338 F1: 0.9251 Train AUC: 0.9820 Val AUC: 0.9781 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 260 Train Loss: 0.1612 Acc: 0.9233 Pre: 0.9096 Recall: 0.9401 F1: 0.9246 Train AUC: 0.9832 Val AUC: 0.9779 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 261 Train Loss: 0.1628 Acc: 0.9254 Pre: 0.9184 Recall: 0.9338 F1: 0.9260 Train AUC: 0.9828 Val AUC: 0.9774 Val PRC: 0.9776 Time: 0.71\n",
      "Epoch: 262 Train Loss: 0.1740 Acc: 0.9217 Pre: 0.9051 Recall: 0.9422 F1: 0.9233 Train AUC: 0.9802 Val AUC: 0.9778 Val PRC: 0.9791 Time: 0.70\n",
      "Epoch: 263 Train Loss: 0.1656 Acc: 0.9254 Pre: 0.9355 Recall: 0.9139 F1: 0.9245 Train AUC: 0.9818 Val AUC: 0.9770 Val PRC: 0.9792 Time: 0.70\n",
      "Epoch: 264 Train Loss: 0.1573 Acc: 0.9196 Pre: 0.9031 Recall: 0.9401 F1: 0.9213 Train AUC: 0.9838 Val AUC: 0.9770 Val PRC: 0.9789 Time: 0.71\n",
      "Epoch: 265 Train Loss: 0.1514 Acc: 0.9244 Pre: 0.9156 Recall: 0.9349 F1: 0.9252 Train AUC: 0.9846 Val AUC: 0.9782 Val PRC: 0.9799 Time: 0.71\n",
      "Epoch: 266 Train Loss: 0.1570 Acc: 0.9207 Pre: 0.9247 Recall: 0.9160 F1: 0.9203 Train AUC: 0.9833 Val AUC: 0.9779 Val PRC: 0.9783 Time: 0.73\n",
      "Epoch: 267 Train Loss: 0.1537 Acc: 0.9212 Pre: 0.9248 Recall: 0.9170 F1: 0.9209 Train AUC: 0.9846 Val AUC: 0.9772 Val PRC: 0.9785 Time: 0.71\n",
      "Epoch: 268 Train Loss: 0.1591 Acc: 0.9286 Pre: 0.9493 Recall: 0.9055 F1: 0.9269 Train AUC: 0.9835 Val AUC: 0.9786 Val PRC: 0.9801 Time: 0.70\n",
      "Epoch: 269 Train Loss: 0.1562 Acc: 0.9270 Pre: 0.9462 Recall: 0.9055 F1: 0.9254 Train AUC: 0.9842 Val AUC: 0.9782 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 270 Train Loss: 0.1519 Acc: 0.9254 Pre: 0.9355 Recall: 0.9139 F1: 0.9245 Train AUC: 0.9853 Val AUC: 0.9770 Val PRC: 0.9784 Time: 0.70\n",
      "Epoch: 271 Train Loss: 0.1554 Acc: 0.9286 Pre: 0.9215 Recall: 0.9370 F1: 0.9292 Train AUC: 0.9841 Val AUC: 0.9769 Val PRC: 0.9774 Time: 0.72\n",
      "Epoch: 272 Train Loss: 0.1591 Acc: 0.9270 Pre: 0.9230 Recall: 0.9317 F1: 0.9273 Train AUC: 0.9829 Val AUC: 0.9784 Val PRC: 0.9797 Time: 0.71\n",
      "Epoch: 273 Train Loss: 0.1586 Acc: 0.9238 Pre: 0.9225 Recall: 0.9254 F1: 0.9240 Train AUC: 0.9825 Val AUC: 0.9768 Val PRC: 0.9772 Time: 0.73\n",
      "Epoch: 274 Train Loss: 0.1541 Acc: 0.9223 Pre: 0.9277 Recall: 0.9160 F1: 0.9218 Train AUC: 0.9842 Val AUC: 0.9769 Val PRC: 0.9785 Time: 0.70\n",
      "Epoch: 275 Train Loss: 0.1386 Acc: 0.9238 Pre: 0.9306 Recall: 0.9160 F1: 0.9232 Train AUC: 0.9875 Val AUC: 0.9783 Val PRC: 0.9802 Time: 0.70\n",
      "Epoch: 276 Train Loss: 0.1624 Acc: 0.9223 Pre: 0.9214 Recall: 0.9233 F1: 0.9224 Train AUC: 0.9826 Val AUC: 0.9768 Val PRC: 0.9791 Time: 0.70\n",
      "Epoch: 277 Train Loss: 0.1513 Acc: 0.9233 Pre: 0.9278 Recall: 0.9181 F1: 0.9229 Train AUC: 0.9853 Val AUC: 0.9766 Val PRC: 0.9786 Time: 0.71\n",
      "Epoch: 278 Train Loss: 0.1546 Acc: 0.9254 Pre: 0.9150 Recall: 0.9380 F1: 0.9263 Train AUC: 0.9841 Val AUC: 0.9765 Val PRC: 0.9789 Time: 0.74\n",
      "Epoch: 279 Train Loss: 0.1450 Acc: 0.9265 Pre: 0.9238 Recall: 0.9296 F1: 0.9267 Train AUC: 0.9863 Val AUC: 0.9763 Val PRC: 0.9773 Time: 0.70\n",
      "Epoch: 280 Train Loss: 0.1439 Acc: 0.9328 Pre: 0.9292 Recall: 0.9370 F1: 0.9331 Train AUC: 0.9860 Val AUC: 0.9794 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 281 Train Loss: 0.1685 Acc: 0.9280 Pre: 0.9415 Recall: 0.9128 F1: 0.9269 Train AUC: 0.9836 Val AUC: 0.9788 Val PRC: 0.9800 Time: 0.71\n",
      "Epoch: 282 Train Loss: 0.1520 Acc: 0.9238 Pre: 0.9088 Recall: 0.9422 F1: 0.9252 Train AUC: 0.9847 Val AUC: 0.9785 Val PRC: 0.9802 Time: 0.71\n",
      "Epoch: 283 Train Loss: 0.1580 Acc: 0.9254 Pre: 0.9272 Recall: 0.9233 F1: 0.9253 Train AUC: 0.9831 Val AUC: 0.9778 Val PRC: 0.9790 Time: 0.71\n",
      "Epoch: 284 Train Loss: 0.1491 Acc: 0.9275 Pre: 0.9257 Recall: 0.9296 F1: 0.9277 Train AUC: 0.9851 Val AUC: 0.9759 Val PRC: 0.9777 Time: 0.73\n",
      "Epoch: 285 Train Loss: 0.1558 Acc: 0.9254 Pre: 0.9263 Recall: 0.9244 F1: 0.9253 Train AUC: 0.9832 Val AUC: 0.9755 Val PRC: 0.9780 Time: 0.72\n",
      "Epoch: 286 Train Loss: 0.1485 Acc: 0.9280 Pre: 0.9321 Recall: 0.9233 F1: 0.9277 Train AUC: 0.9862 Val AUC: 0.9773 Val PRC: 0.9799 Time: 0.74\n",
      "Epoch: 287 Train Loss: 0.1437 Acc: 0.9265 Pre: 0.9375 Recall: 0.9139 F1: 0.9255 Train AUC: 0.9860 Val AUC: 0.9778 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 288 Train Loss: 0.1517 Acc: 0.9228 Pre: 0.9145 Recall: 0.9328 F1: 0.9236 Train AUC: 0.9851 Val AUC: 0.9756 Val PRC: 0.9773 Time: 0.71\n",
      "Epoch: 289 Train Loss: 0.1527 Acc: 0.9249 Pre: 0.9262 Recall: 0.9233 F1: 0.9248 Train AUC: 0.9850 Val AUC: 0.9773 Val PRC: 0.9798 Time: 0.72\n",
      "Epoch: 290 Train Loss: 0.1662 Acc: 0.9249 Pre: 0.9253 Recall: 0.9244 F1: 0.9249 Train AUC: 0.9842 Val AUC: 0.9766 Val PRC: 0.9795 Time: 0.72\n",
      "Epoch: 291 Train Loss: 0.1556 Acc: 0.9249 Pre: 0.9123 Recall: 0.9401 F1: 0.9260 Train AUC: 0.9842 Val AUC: 0.9773 Val PRC: 0.9785 Time: 0.71\n",
      "Epoch: 292 Train Loss: 0.1478 Acc: 0.9223 Pre: 0.9304 Recall: 0.9128 F1: 0.9215 Train AUC: 0.9851 Val AUC: 0.9758 Val PRC: 0.9772 Time: 0.72\n",
      "Epoch: 293 Train Loss: 0.1477 Acc: 0.9238 Pre: 0.9243 Recall: 0.9233 F1: 0.9238 Train AUC: 0.9853 Val AUC: 0.9775 Val PRC: 0.9788 Time: 0.73\n",
      "Epoch: 294 Train Loss: 0.1419 Acc: 0.9270 Pre: 0.9274 Recall: 0.9265 F1: 0.9270 Train AUC: 0.9864 Val AUC: 0.9785 Val PRC: 0.9794 Time: 0.71\n",
      "Epoch: 295 Train Loss: 0.1465 Acc: 0.9259 Pre: 0.9202 Recall: 0.9328 F1: 0.9264 Train AUC: 0.9865 Val AUC: 0.9775 Val PRC: 0.9802 Time: 0.71\n",
      "Epoch: 296 Train Loss: 0.1582 Acc: 0.9238 Pre: 0.9122 Recall: 0.9380 F1: 0.9249 Train AUC: 0.9833 Val AUC: 0.9775 Val PRC: 0.9796 Time: 0.70\n",
      "Epoch: 297 Train Loss: 0.1447 Acc: 0.9202 Pre: 0.9158 Recall: 0.9254 F1: 0.9206 Train AUC: 0.9861 Val AUC: 0.9750 Val PRC: 0.9765 Time: 0.70\n",
      "Epoch: 298 Train Loss: 0.1441 Acc: 0.9270 Pre: 0.9221 Recall: 0.9328 F1: 0.9274 Train AUC: 0.9860 Val AUC: 0.9784 Val PRC: 0.9798 Time: 0.72\n",
      "Epoch: 299 Train Loss: 0.1472 Acc: 0.9175 Pre: 0.9027 Recall: 0.9359 F1: 0.9190 Train AUC: 0.9867 Val AUC: 0.9780 Val PRC: 0.9793 Time: 0.71\n",
      "Epoch: 300 Train Loss: 0.1444 Acc: 0.9223 Pre: 0.9119 Recall: 0.9349 F1: 0.9232 Train AUC: 0.9859 Val AUC: 0.9772 Val PRC: 0.9788 Time: 0.71\n",
      "Epoch: 301 Train Loss: 0.1396 Acc: 0.9270 Pre: 0.9265 Recall: 0.9275 F1: 0.9270 Train AUC: 0.9869 Val AUC: 0.9782 Val PRC: 0.9801 Time: 0.73\n",
      "Epoch: 302 Train Loss: 0.1459 Acc: 0.9249 Pre: 0.9065 Recall: 0.9475 F1: 0.9266 Train AUC: 0.9858 Val AUC: 0.9784 Val PRC: 0.9806 Time: 0.72\n",
      "Epoch: 303 Train Loss: 0.1426 Acc: 0.9296 Pre: 0.9191 Recall: 0.9422 F1: 0.9305 Train AUC: 0.9861 Val AUC: 0.9770 Val PRC: 0.9788 Time: 0.71\n",
      "Epoch: 304 Train Loss: 0.1457 Acc: 0.9275 Pre: 0.9405 Recall: 0.9128 F1: 0.9264 Train AUC: 0.9850 Val AUC: 0.9782 Val PRC: 0.9787 Time: 0.72\n",
      "Epoch: 305 Train Loss: 0.1443 Acc: 0.9270 Pre: 0.9178 Recall: 0.9380 F1: 0.9278 Train AUC: 0.9862 Val AUC: 0.9783 Val PRC: 0.9800 Time: 0.70\n",
      "Epoch: 306 Train Loss: 0.1502 Acc: 0.9286 Pre: 0.9250 Recall: 0.9328 F1: 0.9289 Train AUC: 0.9840 Val AUC: 0.9768 Val PRC: 0.9787 Time: 0.71\n",
      "Epoch: 307 Train Loss: 0.1606 Acc: 0.9275 Pre: 0.9443 Recall: 0.9086 F1: 0.9261 Train AUC: 0.9831 Val AUC: 0.9759 Val PRC: 0.9792 Time: 0.71\n",
      "Epoch: 308 Train Loss: 0.1350 Acc: 0.9275 Pre: 0.9376 Recall: 0.9160 F1: 0.9267 Train AUC: 0.9879 Val AUC: 0.9770 Val PRC: 0.9791 Time: 0.74\n",
      "Epoch: 309 Train Loss: 0.1443 Acc: 0.9275 Pre: 0.9348 Recall: 0.9191 F1: 0.9269 Train AUC: 0.9859 Val AUC: 0.9774 Val PRC: 0.9772 Time: 0.72\n",
      "Epoch: 310 Train Loss: 0.1521 Acc: 0.9291 Pre: 0.9251 Recall: 0.9338 F1: 0.9294 Train AUC: 0.9848 Val AUC: 0.9770 Val PRC: 0.9785 Time: 0.73\n",
      "Epoch: 311 Train Loss: 0.1402 Acc: 0.9312 Pre: 0.9438 Recall: 0.9170 F1: 0.9302 Train AUC: 0.9869 Val AUC: 0.9778 Val PRC: 0.9800 Time: 0.73\n",
      "Epoch: 312 Train Loss: 0.1568 Acc: 0.9286 Pre: 0.9435 Recall: 0.9118 F1: 0.9274 Train AUC: 0.9837 Val AUC: 0.9783 Val PRC: 0.9803 Time: 0.72\n",
      "Epoch: 313 Train Loss: 0.1395 Acc: 0.9228 Pre: 0.9053 Recall: 0.9443 F1: 0.9244 Train AUC: 0.9867 Val AUC: 0.9785 Val PRC: 0.9802 Time: 0.72\n",
      "Epoch: 314 Train Loss: 0.1436 Acc: 0.9270 Pre: 0.9195 Recall: 0.9359 F1: 0.9276 Train AUC: 0.9858 Val AUC: 0.9779 Val PRC: 0.9795 Time: 0.75\n",
      "Epoch: 315 Train Loss: 0.1451 Acc: 0.9244 Pre: 0.9114 Recall: 0.9401 F1: 0.9255 Train AUC: 0.9851 Val AUC: 0.9782 Val PRC: 0.9793 Time: 0.75\n",
      "Epoch: 316 Train Loss: 0.1414 Acc: 0.9338 Pre: 0.9311 Recall: 0.9370 F1: 0.9340 Train AUC: 0.9863 Val AUC: 0.9793 Val PRC: 0.9804 Time: 0.72\n",
      "Epoch: 317 Train Loss: 0.1449 Acc: 0.9254 Pre: 0.9133 Recall: 0.9401 F1: 0.9265 Train AUC: 0.9860 Val AUC: 0.9788 Val PRC: 0.9799 Time: 0.73\n",
      "Epoch: 318 Train Loss: 0.1374 Acc: 0.9265 Pre: 0.9328 Recall: 0.9191 F1: 0.9259 Train AUC: 0.9868 Val AUC: 0.9788 Val PRC: 0.9811 Time: 0.71\n",
      "Epoch: 319 Train Loss: 0.1529 Acc: 0.9228 Pre: 0.9268 Recall: 0.9181 F1: 0.9224 Train AUC: 0.9871 Val AUC: 0.9762 Val PRC: 0.9744 Time: 0.71\n",
      "Epoch: 320 Train Loss: 0.1383 Acc: 0.9207 Pre: 0.9041 Recall: 0.9412 F1: 0.9223 Train AUC: 0.9866 Val AUC: 0.9764 Val PRC: 0.9783 Time: 0.73\n",
      "Epoch: 321 Train Loss: 0.1484 Acc: 0.9286 Pre: 0.9286 Recall: 0.9286 F1: 0.9286 Train AUC: 0.9854 Val AUC: 0.9790 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 322 Train Loss: 0.1405 Acc: 0.9202 Pre: 0.9184 Recall: 0.9223 F1: 0.9203 Train AUC: 0.9869 Val AUC: 0.9775 Val PRC: 0.9805 Time: 0.73\n",
      "Epoch: 323 Train Loss: 0.1406 Acc: 0.9249 Pre: 0.9166 Recall: 0.9349 F1: 0.9256 Train AUC: 0.9859 Val AUC: 0.9766 Val PRC: 0.9789 Time: 0.72\n",
      "Epoch: 324 Train Loss: 0.1318 Acc: 0.9265 Pre: 0.9274 Recall: 0.9254 F1: 0.9264 Train AUC: 0.9887 Val AUC: 0.9760 Val PRC: 0.9781 Time: 0.71\n",
      "Epoch: 325 Train Loss: 0.1237 Acc: 0.9296 Pre: 0.9379 Recall: 0.9202 F1: 0.9290 Train AUC: 0.9895 Val AUC: 0.9789 Val PRC: 0.9810 Time: 0.71\n",
      "Epoch: 326 Train Loss: 0.1424 Acc: 0.9338 Pre: 0.9311 Recall: 0.9370 F1: 0.9340 Train AUC: 0.9855 Val AUC: 0.9809 Val PRC: 0.9833 Time: 0.73\n",
      "Epoch: 327 Train Loss: 0.1249 Acc: 0.9328 Pre: 0.9274 Recall: 0.9391 F1: 0.9332 Train AUC: 0.9892 Val AUC: 0.9785 Val PRC: 0.9812 Time: 0.71\n",
      "Epoch: 328 Train Loss: 0.1273 Acc: 0.9354 Pre: 0.9443 Recall: 0.9254 F1: 0.9347 Train AUC: 0.9885 Val AUC: 0.9791 Val PRC: 0.9812 Time: 0.71\n",
      "Epoch: 329 Train Loss: 0.1399 Acc: 0.9322 Pre: 0.9318 Recall: 0.9328 F1: 0.9323 Train AUC: 0.9865 Val AUC: 0.9790 Val PRC: 0.9811 Time: 0.72\n",
      "Epoch: 330 Train Loss: 0.1191 Acc: 0.9338 Pre: 0.9293 Recall: 0.9391 F1: 0.9342 Train AUC: 0.9899 Val AUC: 0.9788 Val PRC: 0.9798 Time: 0.71\n",
      "Epoch: 331 Train Loss: 0.1361 Acc: 0.9328 Pre: 0.9364 Recall: 0.9286 F1: 0.9325 Train AUC: 0.9871 Val AUC: 0.9794 Val PRC: 0.9809 Time: 0.70\n",
      "Epoch: 332 Train Loss: 0.1330 Acc: 0.9296 Pre: 0.9225 Recall: 0.9380 F1: 0.9302 Train AUC: 0.9875 Val AUC: 0.9782 Val PRC: 0.9793 Time: 0.72\n",
      "Epoch: 333 Train Loss: 0.1346 Acc: 0.9301 Pre: 0.9288 Recall: 0.9317 F1: 0.9303 Train AUC: 0.9876 Val AUC: 0.9789 Val PRC: 0.9805 Time: 0.72\n",
      "Epoch: 334 Train Loss: 0.1353 Acc: 0.9312 Pre: 0.9193 Recall: 0.9454 F1: 0.9322 Train AUC: 0.9872 Val AUC: 0.9804 Val PRC: 0.9818 Time: 0.71\n",
      "Epoch: 335 Train Loss: 0.1226 Acc: 0.9349 Pre: 0.9442 Recall: 0.9244 F1: 0.9342 Train AUC: 0.9894 Val AUC: 0.9792 Val PRC: 0.9815 Time: 0.74\n",
      "Epoch: 336 Train Loss: 0.1263 Acc: 0.9312 Pre: 0.9193 Recall: 0.9454 F1: 0.9322 Train AUC: 0.9887 Val AUC: 0.9790 Val PRC: 0.9812 Time: 0.72\n",
      "Epoch: 337 Train Loss: 0.1324 Acc: 0.9291 Pre: 0.9369 Recall: 0.9202 F1: 0.9285 Train AUC: 0.9872 Val AUC: 0.9794 Val PRC: 0.9802 Time: 0.73\n",
      "Epoch: 338 Train Loss: 0.1350 Acc: 0.9312 Pre: 0.9335 Recall: 0.9286 F1: 0.9310 Train AUC: 0.9866 Val AUC: 0.9793 Val PRC: 0.9802 Time: 0.74\n",
      "Epoch: 339 Train Loss: 0.1276 Acc: 0.9291 Pre: 0.9314 Recall: 0.9265 F1: 0.9289 Train AUC: 0.9882 Val AUC: 0.9793 Val PRC: 0.9804 Time: 0.70\n",
      "Epoch: 340 Train Loss: 0.1308 Acc: 0.9349 Pre: 0.9331 Recall: 0.9370 F1: 0.9350 Train AUC: 0.9878 Val AUC: 0.9793 Val PRC: 0.9807 Time: 0.70\n",
      "Epoch: 341 Train Loss: 0.1279 Acc: 0.9322 Pre: 0.9401 Recall: 0.9233 F1: 0.9316 Train AUC: 0.9879 Val AUC: 0.9799 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 342 Train Loss: 0.1320 Acc: 0.9280 Pre: 0.9162 Recall: 0.9422 F1: 0.9291 Train AUC: 0.9877 Val AUC: 0.9797 Val PRC: 0.9816 Time: 0.72\n",
      "Epoch: 343 Train Loss: 0.1309 Acc: 0.9328 Pre: 0.9374 Recall: 0.9275 F1: 0.9324 Train AUC: 0.9870 Val AUC: 0.9798 Val PRC: 0.9813 Time: 0.75\n",
      "Epoch: 344 Train Loss: 0.1392 Acc: 0.9307 Pre: 0.9298 Recall: 0.9317 F1: 0.9307 Train AUC: 0.9858 Val AUC: 0.9797 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 345 Train Loss: 0.1237 Acc: 0.9370 Pre: 0.9388 Recall: 0.9349 F1: 0.9368 Train AUC: 0.9892 Val AUC: 0.9804 Val PRC: 0.9816 Time: 0.70\n",
      "Epoch: 346 Train Loss: 0.1288 Acc: 0.9359 Pre: 0.9270 Recall: 0.9464 F1: 0.9366 Train AUC: 0.9876 Val AUC: 0.9800 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 347 Train Loss: 0.1267 Acc: 0.9312 Pre: 0.9335 Recall: 0.9286 F1: 0.9310 Train AUC: 0.9885 Val AUC: 0.9813 Val PRC: 0.9814 Time: 0.73\n",
      "Epoch: 348 Train Loss: 0.1279 Acc: 0.9364 Pre: 0.9397 Recall: 0.9328 F1: 0.9362 Train AUC: 0.9884 Val AUC: 0.9807 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 349 Train Loss: 0.1143 Acc: 0.9338 Pre: 0.9197 Recall: 0.9506 F1: 0.9349 Train AUC: 0.9909 Val AUC: 0.9801 Val PRC: 0.9815 Time: 0.71\n",
      "Epoch: 350 Train Loss: 0.1368 Acc: 0.9296 Pre: 0.9342 Recall: 0.9244 F1: 0.9293 Train AUC: 0.9860 Val AUC: 0.9786 Val PRC: 0.9763 Time: 0.73\n",
      "Epoch: 351 Train Loss: 0.1206 Acc: 0.9275 Pre: 0.9395 Recall: 0.9139 F1: 0.9265 Train AUC: 0.9892 Val AUC: 0.9784 Val PRC: 0.9802 Time: 0.93\n",
      "Epoch: 352 Train Loss: 0.1329 Acc: 0.9307 Pre: 0.9307 Recall: 0.9307 F1: 0.9307 Train AUC: 0.9865 Val AUC: 0.9791 Val PRC: 0.9810 Time: 0.72\n",
      "Epoch: 353 Train Loss: 0.1273 Acc: 0.9254 Pre: 0.9108 Recall: 0.9433 F1: 0.9267 Train AUC: 0.9882 Val AUC: 0.9799 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 354 Train Loss: 0.1212 Acc: 0.9317 Pre: 0.9400 Recall: 0.9223 F1: 0.9311 Train AUC: 0.9895 Val AUC: 0.9798 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 355 Train Loss: 0.1247 Acc: 0.9296 Pre: 0.9243 Recall: 0.9359 F1: 0.9301 Train AUC: 0.9889 Val AUC: 0.9801 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 356 Train Loss: 0.1139 Acc: 0.9312 Pre: 0.9344 Recall: 0.9275 F1: 0.9309 Train AUC: 0.9903 Val AUC: 0.9800 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 357 Train Loss: 0.1268 Acc: 0.9349 Pre: 0.9452 Recall: 0.9233 F1: 0.9341 Train AUC: 0.9879 Val AUC: 0.9797 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 358 Train Loss: 0.1146 Acc: 0.9338 Pre: 0.9338 Recall: 0.9338 F1: 0.9338 Train AUC: 0.9900 Val AUC: 0.9789 Val PRC: 0.9799 Time: 0.71\n",
      "Epoch: 359 Train Loss: 0.1250 Acc: 0.9296 Pre: 0.9305 Recall: 0.9286 F1: 0.9295 Train AUC: 0.9881 Val AUC: 0.9777 Val PRC: 0.9791 Time: 0.72\n",
      "Epoch: 360 Train Loss: 0.1253 Acc: 0.9333 Pre: 0.9275 Recall: 0.9401 F1: 0.9338 Train AUC: 0.9885 Val AUC: 0.9787 Val PRC: 0.9797 Time: 0.72\n",
      "Epoch: 361 Train Loss: 0.1302 Acc: 0.9333 Pre: 0.9347 Recall: 0.9317 F1: 0.9332 Train AUC: 0.9875 Val AUC: 0.9802 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 362 Train Loss: 0.1198 Acc: 0.9280 Pre: 0.9214 Recall: 0.9359 F1: 0.9286 Train AUC: 0.9899 Val AUC: 0.9788 Val PRC: 0.9812 Time: 0.71\n",
      "Epoch: 363 Train Loss: 0.1170 Acc: 0.9322 Pre: 0.9291 Recall: 0.9359 F1: 0.9325 Train AUC: 0.9898 Val AUC: 0.9809 Val PRC: 0.9825 Time: 0.73\n",
      "Epoch: 364 Train Loss: 0.1226 Acc: 0.9338 Pre: 0.9293 Recall: 0.9391 F1: 0.9342 Train AUC: 0.9887 Val AUC: 0.9817 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 365 Train Loss: 0.1175 Acc: 0.9322 Pre: 0.9364 Recall: 0.9275 F1: 0.9319 Train AUC: 0.9897 Val AUC: 0.9787 Val PRC: 0.9795 Time: 0.74\n",
      "Epoch: 366 Train Loss: 0.1231 Acc: 0.9270 Pre: 0.9178 Recall: 0.9380 F1: 0.9278 Train AUC: 0.9886 Val AUC: 0.9784 Val PRC: 0.9797 Time: 0.72\n",
      "Epoch: 367 Train Loss: 0.1150 Acc: 0.9291 Pre: 0.9277 Recall: 0.9307 F1: 0.9292 Train AUC: 0.9903 Val AUC: 0.9798 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 368 Train Loss: 0.1256 Acc: 0.9322 Pre: 0.9186 Recall: 0.9485 F1: 0.9333 Train AUC: 0.9882 Val AUC: 0.9789 Val PRC: 0.9806 Time: 0.73\n",
      "Epoch: 369 Train Loss: 0.1158 Acc: 0.9286 Pre: 0.9359 Recall: 0.9202 F1: 0.9280 Train AUC: 0.9903 Val AUC: 0.9796 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 370 Train Loss: 0.1233 Acc: 0.9333 Pre: 0.9257 Recall: 0.9422 F1: 0.9339 Train AUC: 0.9886 Val AUC: 0.9810 Val PRC: 0.9829 Time: 0.74\n",
      "Epoch: 371 Train Loss: 0.1161 Acc: 0.9275 Pre: 0.9170 Recall: 0.9401 F1: 0.9284 Train AUC: 0.9901 Val AUC: 0.9796 Val PRC: 0.9815 Time: 0.73\n",
      "Epoch: 372 Train Loss: 0.1154 Acc: 0.9354 Pre: 0.9405 Recall: 0.9296 F1: 0.9350 Train AUC: 0.9899 Val AUC: 0.9818 Val PRC: 0.9836 Time: 0.73\n",
      "Epoch: 373 Train Loss: 0.1225 Acc: 0.9333 Pre: 0.9213 Recall: 0.9475 F1: 0.9342 Train AUC: 0.9888 Val AUC: 0.9803 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 374 Train Loss: 0.1353 Acc: 0.9375 Pre: 0.9426 Recall: 0.9317 F1: 0.9371 Train AUC: 0.9882 Val AUC: 0.9817 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 375 Train Loss: 0.1170 Acc: 0.9370 Pre: 0.9262 Recall: 0.9496 F1: 0.9378 Train AUC: 0.9894 Val AUC: 0.9803 Val PRC: 0.9822 Time: 0.73\n",
      "Epoch: 376 Train Loss: 0.1151 Acc: 0.9317 Pre: 0.9220 Recall: 0.9433 F1: 0.9325 Train AUC: 0.9901 Val AUC: 0.9797 Val PRC: 0.9804 Time: 0.74\n",
      "Epoch: 377 Train Loss: 0.1214 Acc: 0.9312 Pre: 0.9400 Recall: 0.9212 F1: 0.9305 Train AUC: 0.9891 Val AUC: 0.9795 Val PRC: 0.9805 Time: 0.73\n",
      "Epoch: 378 Train Loss: 0.1168 Acc: 0.9338 Pre: 0.9338 Recall: 0.9338 F1: 0.9338 Train AUC: 0.9897 Val AUC: 0.9803 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 379 Train Loss: 0.1158 Acc: 0.9349 Pre: 0.9386 Recall: 0.9307 F1: 0.9346 Train AUC: 0.9898 Val AUC: 0.9801 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 380 Train Loss: 0.1123 Acc: 0.9354 Pre: 0.9501 Recall: 0.9191 F1: 0.9343 Train AUC: 0.9908 Val AUC: 0.9816 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 381 Train Loss: 0.1150 Acc: 0.9328 Pre: 0.9364 Recall: 0.9286 F1: 0.9325 Train AUC: 0.9903 Val AUC: 0.9807 Val PRC: 0.9820 Time: 0.71\n",
      "Epoch: 382 Train Loss: 0.1254 Acc: 0.9317 Pre: 0.9391 Recall: 0.9233 F1: 0.9311 Train AUC: 0.9874 Val AUC: 0.9801 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 383 Train Loss: 0.1209 Acc: 0.9286 Pre: 0.9340 Recall: 0.9223 F1: 0.9281 Train AUC: 0.9888 Val AUC: 0.9784 Val PRC: 0.9797 Time: 0.72\n",
      "Epoch: 384 Train Loss: 0.1144 Acc: 0.9343 Pre: 0.9303 Recall: 0.9391 F1: 0.9347 Train AUC: 0.9903 Val AUC: 0.9795 Val PRC: 0.9810 Time: 0.73\n",
      "Epoch: 385 Train Loss: 0.1201 Acc: 0.9333 Pre: 0.9347 Recall: 0.9317 F1: 0.9332 Train AUC: 0.9893 Val AUC: 0.9804 Val PRC: 0.9819 Time: 0.72\n",
      "Epoch: 386 Train Loss: 0.1224 Acc: 0.9343 Pre: 0.9294 Recall: 0.9401 F1: 0.9347 Train AUC: 0.9885 Val AUC: 0.9793 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 387 Train Loss: 0.1240 Acc: 0.9333 Pre: 0.9310 Recall: 0.9359 F1: 0.9335 Train AUC: 0.9883 Val AUC: 0.9801 Val PRC: 0.9818 Time: 0.74\n",
      "Epoch: 388 Train Loss: 0.1269 Acc: 0.9317 Pre: 0.9382 Recall: 0.9244 F1: 0.9312 Train AUC: 0.9884 Val AUC: 0.9784 Val PRC: 0.9802 Time: 0.72\n",
      "Epoch: 389 Train Loss: 0.1155 Acc: 0.9328 Pre: 0.9310 Recall: 0.9349 F1: 0.9329 Train AUC: 0.9897 Val AUC: 0.9799 Val PRC: 0.9811 Time: 0.72\n",
      "Epoch: 390 Train Loss: 0.1094 Acc: 0.9328 Pre: 0.9265 Recall: 0.9401 F1: 0.9333 Train AUC: 0.9908 Val AUC: 0.9807 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 391 Train Loss: 0.1277 Acc: 0.9307 Pre: 0.9175 Recall: 0.9464 F1: 0.9317 Train AUC: 0.9877 Val AUC: 0.9779 Val PRC: 0.9787 Time: 0.72\n",
      "Epoch: 392 Train Loss: 0.1217 Acc: 0.9301 Pre: 0.9324 Recall: 0.9275 F1: 0.9300 Train AUC: 0.9883 Val AUC: 0.9807 Val PRC: 0.9817 Time: 0.72\n",
      "Epoch: 393 Train Loss: 0.1170 Acc: 0.9359 Pre: 0.9270 Recall: 0.9464 F1: 0.9366 Train AUC: 0.9897 Val AUC: 0.9829 Val PRC: 0.9845 Time: 0.73\n",
      "Epoch: 394 Train Loss: 0.1147 Acc: 0.9338 Pre: 0.9284 Recall: 0.9401 F1: 0.9342 Train AUC: 0.9907 Val AUC: 0.9817 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 395 Train Loss: 0.1199 Acc: 0.9270 Pre: 0.9061 Recall: 0.9527 F1: 0.9288 Train AUC: 0.9906 Val AUC: 0.9800 Val PRC: 0.9811 Time: 0.72\n",
      "Epoch: 396 Train Loss: 0.1260 Acc: 0.9275 Pre: 0.9162 Recall: 0.9412 F1: 0.9285 Train AUC: 0.9881 Val AUC: 0.9812 Val PRC: 0.9817 Time: 0.73\n",
      "Epoch: 397 Train Loss: 0.1313 Acc: 0.9343 Pre: 0.9321 Recall: 0.9370 F1: 0.9345 Train AUC: 0.9872 Val AUC: 0.9811 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 398 Train Loss: 0.1137 Acc: 0.9359 Pre: 0.9482 Recall: 0.9223 F1: 0.9350 Train AUC: 0.9905 Val AUC: 0.9830 Val PRC: 0.9848 Time: 0.71\n",
      "Epoch: 399 Train Loss: 0.1096 Acc: 0.9317 Pre: 0.9290 Recall: 0.9349 F1: 0.9319 Train AUC: 0.9909 Val AUC: 0.9810 Val PRC: 0.9824 Time: 0.74\n",
      "Epoch: 400 Train Loss: 0.1108 Acc: 0.9349 Pre: 0.9312 Recall: 0.9391 F1: 0.9351 Train AUC: 0.9908 Val AUC: 0.9815 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 401 Train Loss: 0.1139 Acc: 0.9343 Pre: 0.9394 Recall: 0.9286 F1: 0.9340 Train AUC: 0.9896 Val AUC: 0.9799 Val PRC: 0.9811 Time: 0.72\n",
      "Epoch: 402 Train Loss: 0.1206 Acc: 0.9307 Pre: 0.9271 Recall: 0.9349 F1: 0.9310 Train AUC: 0.9889 Val AUC: 0.9814 Val PRC: 0.9828 Time: 0.74\n",
      "Epoch: 403 Train Loss: 0.1200 Acc: 0.9349 Pre: 0.9442 Recall: 0.9244 F1: 0.9342 Train AUC: 0.9894 Val AUC: 0.9813 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 404 Train Loss: 0.1134 Acc: 0.9391 Pre: 0.9419 Recall: 0.9359 F1: 0.9389 Train AUC: 0.9901 Val AUC: 0.9823 Val PRC: 0.9838 Time: 0.73\n",
      "Epoch: 405 Train Loss: 0.1111 Acc: 0.9359 Pre: 0.9368 Recall: 0.9349 F1: 0.9359 Train AUC: 0.9907 Val AUC: 0.9813 Val PRC: 0.9834 Time: 0.72\n",
      "Epoch: 406 Train Loss: 0.1066 Acc: 0.9312 Pre: 0.9254 Recall: 0.9380 F1: 0.9317 Train AUC: 0.9914 Val AUC: 0.9815 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 407 Train Loss: 0.1163 Acc: 0.9359 Pre: 0.9387 Recall: 0.9328 F1: 0.9357 Train AUC: 0.9894 Val AUC: 0.9803 Val PRC: 0.9817 Time: 0.70\n",
      "Epoch: 408 Train Loss: 0.1088 Acc: 0.9349 Pre: 0.9349 Recall: 0.9349 F1: 0.9349 Train AUC: 0.9904 Val AUC: 0.9813 Val PRC: 0.9833 Time: 0.72\n",
      "Epoch: 409 Train Loss: 0.1133 Acc: 0.9333 Pre: 0.9328 Recall: 0.9338 F1: 0.9333 Train AUC: 0.9906 Val AUC: 0.9804 Val PRC: 0.9821 Time: 0.70\n",
      "Epoch: 410 Train Loss: 0.1133 Acc: 0.9328 Pre: 0.9274 Recall: 0.9391 F1: 0.9332 Train AUC: 0.9895 Val AUC: 0.9808 Val PRC: 0.9827 Time: 0.70\n",
      "Epoch: 411 Train Loss: 0.1105 Acc: 0.9333 Pre: 0.9338 Recall: 0.9328 F1: 0.9333 Train AUC: 0.9912 Val AUC: 0.9800 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 412 Train Loss: 0.1078 Acc: 0.9296 Pre: 0.9305 Recall: 0.9286 F1: 0.9295 Train AUC: 0.9912 Val AUC: 0.9816 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 413 Train Loss: 0.1095 Acc: 0.9333 Pre: 0.9328 Recall: 0.9338 F1: 0.9333 Train AUC: 0.9908 Val AUC: 0.9805 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 414 Train Loss: 0.1178 Acc: 0.9349 Pre: 0.9481 Recall: 0.9202 F1: 0.9339 Train AUC: 0.9891 Val AUC: 0.9799 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 415 Train Loss: 0.1127 Acc: 0.9328 Pre: 0.9283 Recall: 0.9380 F1: 0.9331 Train AUC: 0.9906 Val AUC: 0.9819 Val PRC: 0.9839 Time: 0.70\n",
      "Epoch: 416 Train Loss: 0.1145 Acc: 0.9338 Pre: 0.9197 Recall: 0.9506 F1: 0.9349 Train AUC: 0.9902 Val AUC: 0.9797 Val PRC: 0.9820 Time: 0.70\n",
      "Epoch: 417 Train Loss: 0.1119 Acc: 0.9359 Pre: 0.9243 Recall: 0.9496 F1: 0.9368 Train AUC: 0.9900 Val AUC: 0.9809 Val PRC: 0.9827 Time: 0.70\n",
      "Epoch: 418 Train Loss: 0.1097 Acc: 0.9359 Pre: 0.9167 Recall: 0.9590 F1: 0.9374 Train AUC: 0.9907 Val AUC: 0.9825 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 419 Train Loss: 0.1133 Acc: 0.9364 Pre: 0.9521 Recall: 0.9191 F1: 0.9353 Train AUC: 0.9905 Val AUC: 0.9805 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 420 Train Loss: 0.1076 Acc: 0.9386 Pre: 0.9381 Recall: 0.9391 F1: 0.9386 Train AUC: 0.9920 Val AUC: 0.9823 Val PRC: 0.9841 Time: 0.71\n",
      "Epoch: 421 Train Loss: 0.1115 Acc: 0.9364 Pre: 0.9297 Recall: 0.9443 F1: 0.9369 Train AUC: 0.9905 Val AUC: 0.9810 Val PRC: 0.9823 Time: 0.71\n",
      "Epoch: 422 Train Loss: 0.1051 Acc: 0.9407 Pre: 0.9374 Recall: 0.9443 F1: 0.9409 Train AUC: 0.9911 Val AUC: 0.9827 Val PRC: 0.9840 Time: 0.71\n",
      "Epoch: 423 Train Loss: 0.1081 Acc: 0.9307 Pre: 0.9184 Recall: 0.9454 F1: 0.9317 Train AUC: 0.9909 Val AUC: 0.9795 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 424 Train Loss: 0.1210 Acc: 0.9349 Pre: 0.9224 Recall: 0.9496 F1: 0.9358 Train AUC: 0.9890 Val AUC: 0.9809 Val PRC: 0.9823 Time: 0.73\n",
      "Epoch: 425 Train Loss: 0.1152 Acc: 0.9349 Pre: 0.9490 Recall: 0.9191 F1: 0.9338 Train AUC: 0.9903 Val AUC: 0.9804 Val PRC: 0.9822 Time: 0.74\n",
      "Epoch: 426 Train Loss: 0.1004 Acc: 0.9364 Pre: 0.9434 Recall: 0.9286 F1: 0.9359 Train AUC: 0.9920 Val AUC: 0.9814 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 427 Train Loss: 0.1032 Acc: 0.9343 Pre: 0.9499 Recall: 0.9170 F1: 0.9332 Train AUC: 0.9921 Val AUC: 0.9816 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 428 Train Loss: 0.1085 Acc: 0.9354 Pre: 0.9260 Recall: 0.9464 F1: 0.9361 Train AUC: 0.9906 Val AUC: 0.9826 Val PRC: 0.9844 Time: 0.73\n",
      "Epoch: 429 Train Loss: 0.1149 Acc: 0.9396 Pre: 0.9346 Recall: 0.9454 F1: 0.9399 Train AUC: 0.9899 Val AUC: 0.9830 Val PRC: 0.9844 Time: 0.73\n",
      "Epoch: 430 Train Loss: 0.1042 Acc: 0.9370 Pre: 0.9245 Recall: 0.9517 F1: 0.9379 Train AUC: 0.9917 Val AUC: 0.9818 Val PRC: 0.9831 Time: 0.74\n",
      "Epoch: 431 Train Loss: 0.1093 Acc: 0.9359 Pre: 0.9235 Recall: 0.9506 F1: 0.9369 Train AUC: 0.9903 Val AUC: 0.9804 Val PRC: 0.9816 Time: 0.73\n",
      "Epoch: 432 Train Loss: 0.1102 Acc: 0.9296 Pre: 0.9243 Recall: 0.9359 F1: 0.9301 Train AUC: 0.9896 Val AUC: 0.9800 Val PRC: 0.9819 Time: 0.74\n",
      "Epoch: 433 Train Loss: 0.1034 Acc: 0.9338 Pre: 0.9375 Recall: 0.9296 F1: 0.9335 Train AUC: 0.9916 Val AUC: 0.9798 Val PRC: 0.9816 Time: 0.74\n",
      "Epoch: 434 Train Loss: 0.1028 Acc: 0.9370 Pre: 0.9397 Recall: 0.9338 F1: 0.9368 Train AUC: 0.9922 Val AUC: 0.9812 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 435 Train Loss: 0.1022 Acc: 0.9322 Pre: 0.9264 Recall: 0.9391 F1: 0.9327 Train AUC: 0.9918 Val AUC: 0.9796 Val PRC: 0.9821 Time: 0.74\n",
      "Epoch: 436 Train Loss: 0.1016 Acc: 0.9322 Pre: 0.9392 Recall: 0.9244 F1: 0.9317 Train AUC: 0.9921 Val AUC: 0.9797 Val PRC: 0.9823 Time: 0.72\n",
      "Epoch: 437 Train Loss: 0.0967 Acc: 0.9312 Pre: 0.9536 Recall: 0.9065 F1: 0.9295 Train AUC: 0.9928 Val AUC: 0.9802 Val PRC: 0.9820 Time: 0.71\n",
      "Epoch: 438 Train Loss: 0.1047 Acc: 0.9359 Pre: 0.9415 Recall: 0.9296 F1: 0.9355 Train AUC: 0.9915 Val AUC: 0.9812 Val PRC: 0.9842 Time: 0.70\n",
      "Epoch: 439 Train Loss: 0.1092 Acc: 0.9307 Pre: 0.9184 Recall: 0.9454 F1: 0.9317 Train AUC: 0.9901 Val AUC: 0.9821 Val PRC: 0.9834 Time: 0.72\n",
      "Epoch: 440 Train Loss: 0.1102 Acc: 0.9328 Pre: 0.9265 Recall: 0.9401 F1: 0.9333 Train AUC: 0.9906 Val AUC: 0.9811 Val PRC: 0.9824 Time: 0.71\n",
      "Epoch: 441 Train Loss: 0.1015 Acc: 0.9307 Pre: 0.9362 Recall: 0.9244 F1: 0.9302 Train AUC: 0.9915 Val AUC: 0.9778 Val PRC: 0.9777 Time: 0.71\n",
      "Epoch: 442 Train Loss: 0.1067 Acc: 0.9312 Pre: 0.9344 Recall: 0.9275 F1: 0.9309 Train AUC: 0.9905 Val AUC: 0.9819 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 443 Train Loss: 0.0940 Acc: 0.9364 Pre: 0.9601 Recall: 0.9107 F1: 0.9348 Train AUC: 0.9931 Val AUC: 0.9825 Val PRC: 0.9841 Time: 0.72\n",
      "Epoch: 444 Train Loss: 0.1056 Acc: 0.9343 Pre: 0.9539 Recall: 0.9128 F1: 0.9329 Train AUC: 0.9907 Val AUC: 0.9814 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 445 Train Loss: 0.1056 Acc: 0.9338 Pre: 0.9293 Recall: 0.9391 F1: 0.9342 Train AUC: 0.9908 Val AUC: 0.9803 Val PRC: 0.9798 Time: 0.72\n",
      "Epoch: 446 Train Loss: 0.1000 Acc: 0.9370 Pre: 0.9416 Recall: 0.9317 F1: 0.9366 Train AUC: 0.9915 Val AUC: 0.9829 Val PRC: 0.9834 Time: 0.72\n",
      "Epoch: 447 Train Loss: 0.0962 Acc: 0.9407 Pre: 0.9439 Recall: 0.9370 F1: 0.9404 Train AUC: 0.9922 Val AUC: 0.9847 Val PRC: 0.9866 Time: 0.73\n",
      "Epoch: 448 Train Loss: 0.1057 Acc: 0.9359 Pre: 0.9462 Recall: 0.9244 F1: 0.9352 Train AUC: 0.9909 Val AUC: 0.9821 Val PRC: 0.9839 Time: 0.72\n",
      "Epoch: 449 Train Loss: 0.1068 Acc: 0.9375 Pre: 0.9474 Recall: 0.9265 F1: 0.9368 Train AUC: 0.9907 Val AUC: 0.9826 Val PRC: 0.9840 Time: 0.71\n",
      "Epoch: 450 Train Loss: 0.1006 Acc: 0.9375 Pre: 0.9455 Recall: 0.9286 F1: 0.9369 Train AUC: 0.9918 Val AUC: 0.9826 Val PRC: 0.9801 Time: 0.71\n",
      "Epoch: 451 Train Loss: 0.0961 Acc: 0.9391 Pre: 0.9475 Recall: 0.9296 F1: 0.9385 Train AUC: 0.9924 Val AUC: 0.9838 Val PRC: 0.9851 Time: 0.72\n",
      "Epoch: 452 Train Loss: 0.1077 Acc: 0.9338 Pre: 0.9275 Recall: 0.9412 F1: 0.9343 Train AUC: 0.9900 Val AUC: 0.9821 Val PRC: 0.9843 Time: 0.71\n",
      "Epoch: 453 Train Loss: 0.1005 Acc: 0.9349 Pre: 0.9304 Recall: 0.9401 F1: 0.9352 Train AUC: 0.9918 Val AUC: 0.9812 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 454 Train Loss: 0.1014 Acc: 0.9333 Pre: 0.9431 Recall: 0.9223 F1: 0.9326 Train AUC: 0.9918 Val AUC: 0.9799 Val PRC: 0.9824 Time: 0.74\n",
      "Epoch: 455 Train Loss: 0.1081 Acc: 0.9307 Pre: 0.9192 Recall: 0.9443 F1: 0.9316 Train AUC: 0.9904 Val AUC: 0.9805 Val PRC: 0.9824 Time: 0.73\n",
      "Epoch: 456 Train Loss: 0.1049 Acc: 0.9291 Pre: 0.9057 Recall: 0.9580 F1: 0.9311 Train AUC: 0.9927 Val AUC: 0.9807 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 457 Train Loss: 0.1011 Acc: 0.9401 Pre: 0.9420 Recall: 0.9380 F1: 0.9400 Train AUC: 0.9915 Val AUC: 0.9816 Val PRC: 0.9843 Time: 0.72\n",
      "Epoch: 458 Train Loss: 0.1081 Acc: 0.9391 Pre: 0.9504 Recall: 0.9265 F1: 0.9383 Train AUC: 0.9903 Val AUC: 0.9819 Val PRC: 0.9847 Time: 0.71\n",
      "Epoch: 459 Train Loss: 0.0947 Acc: 0.9375 Pre: 0.9426 Recall: 0.9317 F1: 0.9371 Train AUC: 0.9924 Val AUC: 0.9807 Val PRC: 0.9831 Time: 0.73\n",
      "Epoch: 460 Train Loss: 0.0923 Acc: 0.9370 Pre: 0.9454 Recall: 0.9275 F1: 0.9364 Train AUC: 0.9930 Val AUC: 0.9814 Val PRC: 0.9835 Time: 0.74\n",
      "Epoch: 461 Train Loss: 0.0951 Acc: 0.9317 Pre: 0.9220 Recall: 0.9433 F1: 0.9325 Train AUC: 0.9923 Val AUC: 0.9822 Val PRC: 0.9842 Time: 0.73\n",
      "Epoch: 462 Train Loss: 0.1022 Acc: 0.9380 Pre: 0.9427 Recall: 0.9328 F1: 0.9377 Train AUC: 0.9904 Val AUC: 0.9826 Val PRC: 0.9839 Time: 0.71\n",
      "Epoch: 463 Train Loss: 0.0998 Acc: 0.9343 Pre: 0.9357 Recall: 0.9328 F1: 0.9342 Train AUC: 0.9906 Val AUC: 0.9810 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 464 Train Loss: 0.1010 Acc: 0.9328 Pre: 0.9239 Recall: 0.9433 F1: 0.9335 Train AUC: 0.9915 Val AUC: 0.9805 Val PRC: 0.9819 Time: 0.73\n",
      "Epoch: 465 Train Loss: 0.1000 Acc: 0.9349 Pre: 0.9395 Recall: 0.9296 F1: 0.9345 Train AUC: 0.9916 Val AUC: 0.9820 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 466 Train Loss: 0.0980 Acc: 0.9343 Pre: 0.9404 Recall: 0.9275 F1: 0.9339 Train AUC: 0.9922 Val AUC: 0.9816 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 467 Train Loss: 0.0991 Acc: 0.9312 Pre: 0.9353 Recall: 0.9265 F1: 0.9309 Train AUC: 0.9917 Val AUC: 0.9811 Val PRC: 0.9831 Time: 0.73\n",
      "Epoch: 468 Train Loss: 0.1001 Acc: 0.9359 Pre: 0.9424 Recall: 0.9286 F1: 0.9354 Train AUC: 0.9915 Val AUC: 0.9813 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 469 Train Loss: 0.0991 Acc: 0.9338 Pre: 0.9499 Recall: 0.9160 F1: 0.9326 Train AUC: 0.9923 Val AUC: 0.9814 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 470 Train Loss: 0.1102 Acc: 0.9307 Pre: 0.9218 Recall: 0.9412 F1: 0.9314 Train AUC: 0.9917 Val AUC: 0.9815 Val PRC: 0.9831 Time: 0.71\n",
      "Epoch: 471 Train Loss: 0.1128 Acc: 0.9375 Pre: 0.9298 Recall: 0.9464 F1: 0.9381 Train AUC: 0.9896 Val AUC: 0.9824 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 472 Train Loss: 0.0895 Acc: 0.9391 Pre: 0.9514 Recall: 0.9254 F1: 0.9382 Train AUC: 0.9934 Val AUC: 0.9825 Val PRC: 0.9849 Time: 0.70\n",
      "Epoch: 473 Train Loss: 0.1028 Acc: 0.9422 Pre: 0.9469 Recall: 0.9370 F1: 0.9419 Train AUC: 0.9913 Val AUC: 0.9829 Val PRC: 0.9853 Time: 0.71\n",
      "Epoch: 474 Train Loss: 0.0932 Acc: 0.9375 Pre: 0.9398 Recall: 0.9349 F1: 0.9373 Train AUC: 0.9929 Val AUC: 0.9819 Val PRC: 0.9836 Time: 0.72\n",
      "Epoch: 475 Train Loss: 0.0928 Acc: 0.9301 Pre: 0.9226 Recall: 0.9391 F1: 0.9308 Train AUC: 0.9932 Val AUC: 0.9809 Val PRC: 0.9826 Time: 0.70\n",
      "Epoch: 476 Train Loss: 0.1069 Acc: 0.9396 Pre: 0.9401 Recall: 0.9391 F1: 0.9396 Train AUC: 0.9918 Val AUC: 0.9829 Val PRC: 0.9843 Time: 0.71\n",
      "Epoch: 477 Train Loss: 0.0981 Acc: 0.9333 Pre: 0.9239 Recall: 0.9443 F1: 0.9340 Train AUC: 0.9922 Val AUC: 0.9834 Val PRC: 0.9852 Time: 0.70\n",
      "Epoch: 478 Train Loss: 0.1049 Acc: 0.9391 Pre: 0.9419 Recall: 0.9359 F1: 0.9389 Train AUC: 0.9908 Val AUC: 0.9816 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 479 Train Loss: 0.0963 Acc: 0.9317 Pre: 0.9429 Recall: 0.9191 F1: 0.9309 Train AUC: 0.9922 Val AUC: 0.9817 Val PRC: 0.9837 Time: 0.71\n",
      "Epoch: 480 Train Loss: 0.0932 Acc: 0.9391 Pre: 0.9382 Recall: 0.9401 F1: 0.9391 Train AUC: 0.9926 Val AUC: 0.9816 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 481 Train Loss: 0.1010 Acc: 0.9386 Pre: 0.9291 Recall: 0.9496 F1: 0.9392 Train AUC: 0.9915 Val AUC: 0.9826 Val PRC: 0.9836 Time: 0.72\n",
      "Epoch: 482 Train Loss: 0.1018 Acc: 0.9338 Pre: 0.9293 Recall: 0.9391 F1: 0.9342 Train AUC: 0.9910 Val AUC: 0.9805 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 483 Train Loss: 0.0969 Acc: 0.9354 Pre: 0.9481 Recall: 0.9212 F1: 0.9345 Train AUC: 0.9924 Val AUC: 0.9799 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 484 Train Loss: 0.0956 Acc: 0.9396 Pre: 0.9495 Recall: 0.9286 F1: 0.9389 Train AUC: 0.9921 Val AUC: 0.9817 Val PRC: 0.9837 Time: 0.92\n",
      "Epoch: 485 Train Loss: 0.1054 Acc: 0.9349 Pre: 0.9268 Recall: 0.9443 F1: 0.9355 Train AUC: 0.9905 Val AUC: 0.9819 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 486 Train Loss: 0.0957 Acc: 0.9349 Pre: 0.9442 Recall: 0.9244 F1: 0.9342 Train AUC: 0.9922 Val AUC: 0.9823 Val PRC: 0.9834 Time: 0.72\n",
      "Epoch: 487 Train Loss: 0.0982 Acc: 0.9375 Pre: 0.9281 Recall: 0.9485 F1: 0.9382 Train AUC: 0.9913 Val AUC: 0.9824 Val PRC: 0.9835 Time: 0.70\n",
      "Epoch: 488 Train Loss: 0.0973 Acc: 0.9370 Pre: 0.9435 Recall: 0.9296 F1: 0.9365 Train AUC: 0.9922 Val AUC: 0.9823 Val PRC: 0.9832 Time: 0.73\n",
      "Epoch: 489 Train Loss: 0.0963 Acc: 0.9343 Pre: 0.9461 Recall: 0.9212 F1: 0.9335 Train AUC: 0.9921 Val AUC: 0.9834 Val PRC: 0.9849 Time: 0.71\n",
      "Epoch: 490 Train Loss: 0.1034 Acc: 0.9307 Pre: 0.9362 Recall: 0.9244 F1: 0.9302 Train AUC: 0.9906 Val AUC: 0.9805 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 491 Train Loss: 0.0948 Acc: 0.9333 Pre: 0.9431 Recall: 0.9223 F1: 0.9326 Train AUC: 0.9924 Val AUC: 0.9828 Val PRC: 0.9843 Time: 0.72\n",
      "Epoch: 492 Train Loss: 0.0865 Acc: 0.9359 Pre: 0.9314 Recall: 0.9412 F1: 0.9363 Train AUC: 0.9939 Val AUC: 0.9807 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 493 Train Loss: 0.0964 Acc: 0.9359 Pre: 0.9243 Recall: 0.9496 F1: 0.9368 Train AUC: 0.9917 Val AUC: 0.9830 Val PRC: 0.9839 Time: 0.71\n",
      "Epoch: 494 Train Loss: 0.1155 Acc: 0.9396 Pre: 0.9382 Recall: 0.9412 F1: 0.9397 Train AUC: 0.9907 Val AUC: 0.9832 Val PRC: 0.9851 Time: 0.72\n",
      "Epoch: 495 Train Loss: 0.1066 Acc: 0.9349 Pre: 0.9242 Recall: 0.9475 F1: 0.9357 Train AUC: 0.9905 Val AUC: 0.9821 Val PRC: 0.9836 Time: 0.71\n",
      "Epoch: 496 Train Loss: 0.0955 Acc: 0.9349 Pre: 0.9216 Recall: 0.9506 F1: 0.9359 Train AUC: 0.9923 Val AUC: 0.9813 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 497 Train Loss: 0.0963 Acc: 0.9338 Pre: 0.9394 Recall: 0.9275 F1: 0.9334 Train AUC: 0.9923 Val AUC: 0.9819 Val PRC: 0.9832 Time: 0.72\n",
      "Epoch: 498 Train Loss: 0.0901 Acc: 0.9359 Pre: 0.9387 Recall: 0.9328 F1: 0.9357 Train AUC: 0.9934 Val AUC: 0.9815 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 499 Train Loss: 0.0940 Acc: 0.9317 Pre: 0.9246 Recall: 0.9401 F1: 0.9323 Train AUC: 0.9927 Val AUC: 0.9822 Val PRC: 0.9831 Time: 0.70\n",
      "Epoch: 500 Train Loss: 0.0938 Acc: 0.9386 Pre: 0.9475 Recall: 0.9286 F1: 0.9379 Train AUC: 0.9921 Val AUC: 0.9834 Val PRC: 0.9853 Time: 0.71\n",
      "Fold: 4 Best Epoch: 447 Val acc: 0.9407 Val Pre: 0.9439 Val Recall: 0.9370 Val F1: 0.9404 Val AUC: 0.9847 Val PRC: 0.9866\n",
      "------this is 5th cross validation------\n",
      "total params: 307522\n"
     ]
    },
   
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Train Loss: 0.7042 Acc: 0.5005 Pre: 0.5003 Recall: 1.0000 F1: 0.6669 Train AUC: 0.5020 Val AUC: 0.4754 Val PRC: 0.4734 Time: 0.74\n",
      "Epoch: 2 Train Loss: 0.7108 Acc: 0.5016 Pre: 0.5008 Recall: 1.0000 F1: 0.6674 Train AUC: 0.4979 Val AUC: 0.4752 Val PRC: 0.4674 Time: 0.73\n",
      "Epoch: 3 Train Loss: 0.7002 Acc: 0.5047 Pre: 0.5024 Recall: 0.9990 F1: 0.6685 Train AUC: 0.5162 Val AUC: 0.4856 Val PRC: 0.4828 Time: 0.73\n",
      "Epoch: 4 Train Loss: 0.6696 Acc: 0.5696 Pre: 0.5397 Recall: 0.9476 F1: 0.6877 Train AUC: 0.6639 Val AUC: 0.6288 Val PRC: 0.5783 Time: 0.74\n",
      "Epoch: 5 Train Loss: 0.6869 Acc: 0.5188 Pre: 0.5098 Recall: 0.9853 F1: 0.6719 Train AUC: 0.5821 Val AUC: 0.5520 Val PRC: 0.5263 Time: 0.73\n",
      "Epoch: 6 Train Loss: 0.6674 Acc: 0.5880 Pre: 0.5519 Recall: 0.9351 F1: 0.6941 Train AUC: 0.6714 Val AUC: 0.6513 Val PRC: 0.6099 Time: 0.73\n",
      "Epoch: 7 Train Loss: 0.6708 Acc: 0.5492 Pre: 0.5262 Recall: 0.9895 F1: 0.6870 Train AUC: 0.6470 Val AUC: 0.6138 Val PRC: 0.5833 Time: 0.76\n",
      "Epoch: 8 Train Loss: 0.6866 Acc: 0.5157 Pre: 0.5081 Recall: 0.9801 F1: 0.6693 Train AUC: 0.5826 Val AUC: 0.5654 Val PRC: 0.5549 Time: 0.72\n",
      "Epoch: 9 Train Loss: 0.6676 Acc: 0.5414 Pre: 0.5218 Recall: 0.9885 F1: 0.6831 Train AUC: 0.6586 Val AUC: 0.6287 Val PRC: 0.6124 Time: 0.73\n",
      "Epoch: 10 Train Loss: 0.6485 Acc: 0.6016 Pre: 0.5596 Recall: 0.9539 F1: 0.7054 Train AUC: 0.7371 Val AUC: 0.7223 Val PRC: 0.7092 Time: 0.73\n",
      "Epoch: 11 Train Loss: 0.6701 Acc: 0.5482 Pre: 0.5263 Recall: 0.9634 F1: 0.6807 Train AUC: 0.6395 Val AUC: 0.6203 Val PRC: 0.6097 Time: 0.72\n",
      "Epoch: 12 Train Loss: 0.6417 Acc: 0.6073 Pre: 0.5642 Recall: 0.9435 F1: 0.7061 Train AUC: 0.7541 Val AUC: 0.7378 Val PRC: 0.7267 Time: 0.74\n",
      "Epoch: 13 Train Loss: 0.6437 Acc: 0.5953 Pre: 0.5544 Recall: 0.9717 F1: 0.7060 Train AUC: 0.7446 Val AUC: 0.7219 Val PRC: 0.7034 Time: 0.73\n",
      "Epoch: 14 Train Loss: 0.6323 Acc: 0.6482 Pre: 0.6019 Recall: 0.8754 F1: 0.7133 Train AUC: 0.7607 Val AUC: 0.7541 Val PRC: 0.7592 Time: 0.74\n",
      "Epoch: 15 Train Loss: 0.6189 Acc: 0.6754 Pre: 0.6320 Recall: 0.8398 F1: 0.7212 Train AUC: 0.7945 Val AUC: 0.7864 Val PRC: 0.8066 Time: 0.73\n",
      "Epoch: 16 Train Loss: 0.6261 Acc: 0.6461 Pre: 0.5936 Recall: 0.9267 F1: 0.7236 Train AUC: 0.7827 Val AUC: 0.7729 Val PRC: 0.7685 Time: 0.74\n",
      "Epoch: 17 Train Loss: 0.6080 Acc: 0.7037 Pre: 0.6751 Recall: 0.7853 F1: 0.7260 Train AUC: 0.8145 Val AUC: 0.7998 Val PRC: 0.8044 Time: 0.73\n",
      "Epoch: 18 Train Loss: 0.6071 Acc: 0.6906 Pre: 0.6426 Recall: 0.8586 F1: 0.7351 Train AUC: 0.8068 Val AUC: 0.7951 Val PRC: 0.8088 Time: 0.73\n",
      "Epoch: 19 Train Loss: 0.5948 Acc: 0.6942 Pre: 0.6428 Recall: 0.8743 F1: 0.7409 Train AUC: 0.8235 Val AUC: 0.8212 Val PRC: 0.8379 Time: 0.72\n",
      "Epoch: 20 Train Loss: 0.5820 Acc: 0.7042 Pre: 0.6558 Recall: 0.8597 F1: 0.7440 Train AUC: 0.8315 Val AUC: 0.8317 Val PRC: 0.8529 Time: 0.72\n",
      "Epoch: 21 Train Loss: 0.5774 Acc: 0.6979 Pre: 0.6479 Recall: 0.8670 F1: 0.7416 Train AUC: 0.8321 Val AUC: 0.8228 Val PRC: 0.8407 Time: 0.74\n",
      "Epoch: 22 Train Loss: 0.5904 Acc: 0.6958 Pre: 0.6400 Recall: 0.8953 F1: 0.7464 Train AUC: 0.8181 Val AUC: 0.8149 Val PRC: 0.8165 Time: 0.71\n",
      "Epoch: 23 Train Loss: 0.5602 Acc: 0.6984 Pre: 0.6470 Recall: 0.8733 F1: 0.7433 Train AUC: 0.8299 Val AUC: 0.8287 Val PRC: 0.8504 Time: 0.72\n",
      "Epoch: 24 Train Loss: 0.5532 Acc: 0.7319 Pre: 0.6885 Recall: 0.8471 F1: 0.7596 Train AUC: 0.8430 Val AUC: 0.8420 Val PRC: 0.8627 Time: 0.73\n",
      "Epoch: 25 Train Loss: 0.5450 Acc: 0.7204 Pre: 0.6724 Recall: 0.8597 F1: 0.7546 Train AUC: 0.8419 Val AUC: 0.8417 Val PRC: 0.8636 Time: 0.73\n",
      "Epoch: 26 Train Loss: 0.5333 Acc: 0.7257 Pre: 0.6885 Recall: 0.8241 F1: 0.7502 Train AUC: 0.8450 Val AUC: 0.8402 Val PRC: 0.8682 Time: 0.72\n",
      "Epoch: 27 Train Loss: 0.5277 Acc: 0.7293 Pre: 0.6959 Recall: 0.8147 F1: 0.7506 Train AUC: 0.8470 Val AUC: 0.8417 Val PRC: 0.8663 Time: 0.74\n",
      "Epoch: 28 Train Loss: 0.5199 Acc: 0.7204 Pre: 0.6659 Recall: 0.8848 F1: 0.7599 Train AUC: 0.8537 Val AUC: 0.8509 Val PRC: 0.8748 Time: 0.71\n",
      "Epoch: 29 Train Loss: 0.5260 Acc: 0.7335 Pre: 0.7061 Recall: 0.8000 F1: 0.7501 Train AUC: 0.8347 Val AUC: 0.8388 Val PRC: 0.8616 Time: 0.72\n",
      "Epoch: 30 Train Loss: 0.5080 Acc: 0.7398 Pre: 0.7116 Recall: 0.8063 F1: 0.7560 Train AUC: 0.8498 Val AUC: 0.8474 Val PRC: 0.8695 Time: 0.71\n",
      "Epoch: 31 Train Loss: 0.4959 Acc: 0.7356 Pre: 0.7031 Recall: 0.8157 F1: 0.7552 Train AUC: 0.8547 Val AUC: 0.8521 Val PRC: 0.8752 Time: 0.71\n",
      "Epoch: 32 Train Loss: 0.4906 Acc: 0.7147 Pre: 0.6541 Recall: 0.9110 F1: 0.7615 Train AUC: 0.8524 Val AUC: 0.8575 Val PRC: 0.8809 Time: 0.72\n",
      "Epoch: 33 Train Loss: 0.4821 Acc: 0.7241 Pre: 0.6717 Recall: 0.8764 F1: 0.7606 Train AUC: 0.8585 Val AUC: 0.8607 Val PRC: 0.8820 Time: 0.76\n",
      "Epoch: 34 Train Loss: 0.4777 Acc: 0.7597 Pre: 0.7301 Recall: 0.8241 F1: 0.7742 Train AUC: 0.8591 Val AUC: 0.8638 Val PRC: 0.8852 Time: 0.71\n",
      "Epoch: 35 Train Loss: 0.4678 Acc: 0.7529 Pre: 0.7048 Recall: 0.8702 F1: 0.7788 Train AUC: 0.8705 Val AUC: 0.8720 Val PRC: 0.8874 Time: 0.71\n",
      "Epoch: 36 Train Loss: 0.4679 Acc: 0.7545 Pre: 0.7113 Recall: 0.8565 F1: 0.7772 Train AUC: 0.8628 Val AUC: 0.8678 Val PRC: 0.8888 Time: 0.73\n",
      "Epoch: 37 Train Loss: 0.4547 Acc: 0.7764 Pre: 0.7809 Recall: 0.7686 F1: 0.7747 Train AUC: 0.8707 Val AUC: 0.8699 Val PRC: 0.8889 Time: 0.71\n",
      "Epoch: 38 Train Loss: 0.4406 Acc: 0.7859 Pre: 0.7774 Recall: 0.8010 F1: 0.7891 Train AUC: 0.8804 Val AUC: 0.8804 Val PRC: 0.9004 Time: 0.72\n",
      "Epoch: 39 Train Loss: 0.4404 Acc: 0.8079 Pre: 0.8568 Recall: 0.7393 F1: 0.7937 Train AUC: 0.8785 Val AUC: 0.8836 Val PRC: 0.9029 Time: 0.73\n",
      "Epoch: 40 Train Loss: 0.4341 Acc: 0.8042 Pre: 0.8297 Recall: 0.7654 F1: 0.7963 Train AUC: 0.8812 Val AUC: 0.8879 Val PRC: 0.9060 Time: 0.72\n",
      "Epoch: 41 Train Loss: 0.4324 Acc: 0.8188 Pre: 0.8811 Recall: 0.7372 F1: 0.8027 Train AUC: 0.8810 Val AUC: 0.8892 Val PRC: 0.9083 Time: 0.74\n",
      "Epoch: 42 Train Loss: 0.4325 Acc: 0.8131 Pre: 0.9321 Recall: 0.6754 F1: 0.7832 Train AUC: 0.8793 Val AUC: 0.8799 Val PRC: 0.9007 Time: 0.71\n",
      "Epoch: 43 Train Loss: 0.4252 Acc: 0.7932 Pre: 0.7887 Recall: 0.8010 F1: 0.7948 Train AUC: 0.8821 Val AUC: 0.8867 Val PRC: 0.9036 Time: 0.71\n",
      "Epoch: 44 Train Loss: 0.4176 Acc: 0.8173 Pre: 0.8895 Recall: 0.7246 F1: 0.7986 Train AUC: 0.8876 Val AUC: 0.8879 Val PRC: 0.9069 Time: 0.72\n",
      "Epoch: 45 Train Loss: 0.4136 Acc: 0.8010 Pre: 0.8081 Recall: 0.7895 F1: 0.7987 Train AUC: 0.8843 Val AUC: 0.8911 Val PRC: 0.9069 Time: 0.72\n",
      "Epoch: 46 Train Loss: 0.4071 Acc: 0.8052 Pre: 0.8021 Recall: 0.8105 F1: 0.8062 Train AUC: 0.8897 Val AUC: 0.8961 Val PRC: 0.9133 Time: 0.72\n",
      "Epoch: 47 Train Loss: 0.4012 Acc: 0.8335 Pre: 0.9174 Recall: 0.7330 F1: 0.8149 Train AUC: 0.8924 Val AUC: 0.8958 Val PRC: 0.9121 Time: 0.70\n",
      "Epoch: 48 Train Loss: 0.3959 Acc: 0.8220 Pre: 0.8810 Recall: 0.7445 F1: 0.8070 Train AUC: 0.8953 Val AUC: 0.8943 Val PRC: 0.9115 Time: 0.72\n",
      "Epoch: 49 Train Loss: 0.4004 Acc: 0.8047 Pre: 0.7928 Recall: 0.8251 F1: 0.8086 Train AUC: 0.8934 Val AUC: 0.8998 Val PRC: 0.9162 Time: 0.72\n",
      "Epoch: 50 Train Loss: 0.3907 Acc: 0.8319 Pre: 0.9033 Recall: 0.7435 F1: 0.8156 Train AUC: 0.9001 Val AUC: 0.9005 Val PRC: 0.9172 Time: 0.72\n",
      "Epoch: 51 Train Loss: 0.3943 Acc: 0.8110 Pre: 0.8153 Recall: 0.8042 F1: 0.8097 Train AUC: 0.8970 Val AUC: 0.8990 Val PRC: 0.9135 Time: 0.72\n",
      "Epoch: 52 Train Loss: 0.3837 Acc: 0.8277 Pre: 0.8614 Recall: 0.7812 F1: 0.8193 Train AUC: 0.9023 Val AUC: 0.9060 Val PRC: 0.9208 Time: 0.72\n",
      "Epoch: 53 Train Loss: 0.3787 Acc: 0.8199 Pre: 0.8153 Recall: 0.8272 F1: 0.8212 Train AUC: 0.9057 Val AUC: 0.9106 Val PRC: 0.9240 Time: 0.71\n",
      "Epoch: 54 Train Loss: 0.3768 Acc: 0.8298 Pre: 0.8637 Recall: 0.7832 F1: 0.8215 Train AUC: 0.9064 Val AUC: 0.9074 Val PRC: 0.9222 Time: 0.71\n",
      "Epoch: 55 Train Loss: 0.3683 Acc: 0.8372 Pre: 0.8602 Recall: 0.8052 F1: 0.8318 Train AUC: 0.9094 Val AUC: 0.9122 Val PRC: 0.9256 Time: 0.73\n",
      "Epoch: 56 Train Loss: 0.3689 Acc: 0.8372 Pre: 0.8546 Recall: 0.8126 F1: 0.8331 Train AUC: 0.9118 Val AUC: 0.9135 Val PRC: 0.9269 Time: 0.73\n",
      "Epoch: 57 Train Loss: 0.3604 Acc: 0.8330 Pre: 0.8502 Recall: 0.8084 F1: 0.8288 Train AUC: 0.9160 Val AUC: 0.9145 Val PRC: 0.9286 Time: 0.72\n",
      "Epoch: 58 Train Loss: 0.3617 Acc: 0.8471 Pre: 0.8999 Recall: 0.7812 F1: 0.8363 Train AUC: 0.9153 Val AUC: 0.9152 Val PRC: 0.9300 Time: 0.73\n",
      "Epoch: 59 Train Loss: 0.3555 Acc: 0.8476 Pre: 0.8971 Recall: 0.7853 F1: 0.8375 Train AUC: 0.9186 Val AUC: 0.9166 Val PRC: 0.9299 Time: 0.71\n",
      "Epoch: 60 Train Loss: 0.3518 Acc: 0.8419 Pre: 0.8648 Recall: 0.8105 F1: 0.8368 Train AUC: 0.9206 Val AUC: 0.9147 Val PRC: 0.9285 Time: 0.72\n",
      "Epoch: 61 Train Loss: 0.3563 Acc: 0.8403 Pre: 0.8392 Recall: 0.8419 F1: 0.8406 Train AUC: 0.9201 Val AUC: 0.9187 Val PRC: 0.9314 Time: 0.73\n",
      "Epoch: 62 Train Loss: 0.3544 Acc: 0.8398 Pre: 0.8531 Recall: 0.8209 F1: 0.8367 Train AUC: 0.9200 Val AUC: 0.9126 Val PRC: 0.9262 Time: 0.74\n",
      "Epoch: 63 Train Loss: 0.3528 Acc: 0.8435 Pre: 0.8628 Recall: 0.8168 F1: 0.8392 Train AUC: 0.9202 Val AUC: 0.9170 Val PRC: 0.9315 Time: 0.73\n",
      "Epoch: 64 Train Loss: 0.3447 Acc: 0.8492 Pre: 0.8693 Recall: 0.8220 F1: 0.8450 Train AUC: 0.9241 Val AUC: 0.9202 Val PRC: 0.9326 Time: 0.72\n",
      "Epoch: 65 Train Loss: 0.3499 Acc: 0.8455 Pre: 0.8811 Recall: 0.7990 F1: 0.8380 Train AUC: 0.9215 Val AUC: 0.9195 Val PRC: 0.9311 Time: 0.75\n",
      "Epoch: 66 Train Loss: 0.3420 Acc: 0.8429 Pre: 0.8579 Recall: 0.8220 F1: 0.8396 Train AUC: 0.9249 Val AUC: 0.9174 Val PRC: 0.9304 Time: 0.75\n",
      "Epoch: 67 Train Loss: 0.3339 Acc: 0.8555 Pre: 0.9018 Recall: 0.7979 F1: 0.8467 Train AUC: 0.9293 Val AUC: 0.9221 Val PRC: 0.9346 Time: 0.72\n",
      "Epoch: 68 Train Loss: 0.3306 Acc: 0.8555 Pre: 0.8961 Recall: 0.8042 F1: 0.8477 Train AUC: 0.9306 Val AUC: 0.9219 Val PRC: 0.9342 Time: 0.71\n",
      "Epoch: 69 Train Loss: 0.3408 Acc: 0.8534 Pre: 0.8641 Recall: 0.8387 F1: 0.8512 Train AUC: 0.9266 Val AUC: 0.9272 Val PRC: 0.9383 Time: 0.73\n",
      "Epoch: 70 Train Loss: 0.3248 Acc: 0.8471 Pre: 0.8576 Recall: 0.8325 F1: 0.8448 Train AUC: 0.9327 Val AUC: 0.9279 Val PRC: 0.9385 Time: 0.73\n",
      "Epoch: 71 Train Loss: 0.3363 Acc: 0.8613 Pre: 0.8993 Recall: 0.8136 F1: 0.8543 Train AUC: 0.9287 Val AUC: 0.9283 Val PRC: 0.9397 Time: 0.72\n",
      "Epoch: 72 Train Loss: 0.3298 Acc: 0.8628 Pre: 0.8714 Recall: 0.8513 F1: 0.8612 Train AUC: 0.9326 Val AUC: 0.9321 Val PRC: 0.9417 Time: 0.71\n",
      "Epoch: 73 Train Loss: 0.3282 Acc: 0.8607 Pre: 0.8910 Recall: 0.8220 F1: 0.8551 Train AUC: 0.9323 Val AUC: 0.9293 Val PRC: 0.9401 Time: 0.73\n",
      "Epoch: 74 Train Loss: 0.3157 Acc: 0.8665 Pre: 0.8821 Recall: 0.8461 F1: 0.8637 Train AUC: 0.9386 Val AUC: 0.9354 Val PRC: 0.9434 Time: 0.71\n",
      "Epoch: 75 Train Loss: 0.3201 Acc: 0.8597 Pre: 0.8754 Recall: 0.8387 F1: 0.8567 Train AUC: 0.9365 Val AUC: 0.9324 Val PRC: 0.9395 Time: 0.71\n",
      "Epoch: 76 Train Loss: 0.3254 Acc: 0.8565 Pre: 0.8650 Recall: 0.8450 F1: 0.8549 Train AUC: 0.9342 Val AUC: 0.9323 Val PRC: 0.9413 Time: 0.74\n",
      "Epoch: 77 Train Loss: 0.3211 Acc: 0.8440 Pre: 0.8369 Recall: 0.8545 F1: 0.8456 Train AUC: 0.9359 Val AUC: 0.9306 Val PRC: 0.9392 Time: 0.73\n",
      "Epoch: 78 Train Loss: 0.3179 Acc: 0.8602 Pre: 0.8660 Recall: 0.8524 F1: 0.8591 Train AUC: 0.9377 Val AUC: 0.9365 Val PRC: 0.9441 Time: 0.71\n",
      "Epoch: 79 Train Loss: 0.3047 Acc: 0.8660 Pre: 0.8787 Recall: 0.8492 F1: 0.8637 Train AUC: 0.9421 Val AUC: 0.9370 Val PRC: 0.9452 Time: 0.72\n",
      "Epoch: 80 Train Loss: 0.3124 Acc: 0.8565 Pre: 0.8485 Recall: 0.8681 F1: 0.8582 Train AUC: 0.9390 Val AUC: 0.9356 Val PRC: 0.9434 Time: 0.72\n",
      "Epoch: 81 Train Loss: 0.3079 Acc: 0.8675 Pre: 0.9072 Recall: 0.8188 F1: 0.8608 Train AUC: 0.9417 Val AUC: 0.9372 Val PRC: 0.9445 Time: 0.71\n",
      "Epoch: 82 Train Loss: 0.3018 Acc: 0.8529 Pre: 0.8266 Recall: 0.8932 F1: 0.8586 Train AUC: 0.9438 Val AUC: 0.9370 Val PRC: 0.9419 Time: 0.72\n",
      "Epoch: 83 Train Loss: 0.3069 Acc: 0.8592 Pre: 0.8588 Recall: 0.8597 F1: 0.8592 Train AUC: 0.9427 Val AUC: 0.9369 Val PRC: 0.9431 Time: 0.73\n",
      "Epoch: 84 Train Loss: 0.3040 Acc: 0.8675 Pre: 0.8524 Recall: 0.8890 F1: 0.8703 Train AUC: 0.9433 Val AUC: 0.9383 Val PRC: 0.9462 Time: 0.72\n",
      "Epoch: 85 Train Loss: 0.3048 Acc: 0.8639 Pre: 0.8773 Recall: 0.8461 F1: 0.8614 Train AUC: 0.9437 Val AUC: 0.9379 Val PRC: 0.9453 Time: 0.72\n",
      "Epoch: 86 Train Loss: 0.3043 Acc: 0.8607 Pre: 0.8483 Recall: 0.8785 F1: 0.8632 Train AUC: 0.9432 Val AUC: 0.9394 Val PRC: 0.9451 Time: 0.72\n",
      "Epoch: 87 Train Loss: 0.3006 Acc: 0.8398 Pre: 0.7942 Recall: 0.9173 F1: 0.8513 Train AUC: 0.9446 Val AUC: 0.9389 Val PRC: 0.9449 Time: 0.72\n",
      "Epoch: 88 Train Loss: 0.2896 Acc: 0.8628 Pre: 0.8394 Recall: 0.8974 F1: 0.8674 Train AUC: 0.9489 Val AUC: 0.9451 Val PRC: 0.9482 Time: 0.73\n",
      "Epoch: 89 Train Loss: 0.2930 Acc: 0.8618 Pre: 0.8618 Recall: 0.8618 F1: 0.8618 Train AUC: 0.9471 Val AUC: 0.9413 Val PRC: 0.9467 Time: 0.72\n",
      "Epoch: 90 Train Loss: 0.2904 Acc: 0.8634 Pre: 0.8456 Recall: 0.8890 F1: 0.8668 Train AUC: 0.9489 Val AUC: 0.9431 Val PRC: 0.9493 Time: 0.73\n",
      "Epoch: 91 Train Loss: 0.2927 Acc: 0.8712 Pre: 0.8606 Recall: 0.8859 F1: 0.8731 Train AUC: 0.9483 Val AUC: 0.9441 Val PRC: 0.9485 Time: 0.74\n",
      "Epoch: 92 Train Loss: 0.2986 Acc: 0.8675 Pre: 0.8524 Recall: 0.8890 F1: 0.8703 Train AUC: 0.9463 Val AUC: 0.9436 Val PRC: 0.9482 Time: 0.74\n",
      "Epoch: 93 Train Loss: 0.2864 Acc: 0.8702 Pre: 0.8702 Recall: 0.8702 F1: 0.8702 Train AUC: 0.9502 Val AUC: 0.9452 Val PRC: 0.9515 Time: 0.71\n",
      "Epoch: 94 Train Loss: 0.2822 Acc: 0.8754 Pre: 0.8834 Recall: 0.8649 F1: 0.8741 Train AUC: 0.9519 Val AUC: 0.9455 Val PRC: 0.9496 Time: 0.73\n",
      "Epoch: 95 Train Loss: 0.2914 Acc: 0.8654 Pre: 0.8483 Recall: 0.8901 F1: 0.8687 Train AUC: 0.9485 Val AUC: 0.9462 Val PRC: 0.9515 Time: 0.74\n",
      "Epoch: 96 Train Loss: 0.2881 Acc: 0.8686 Pre: 0.8506 Recall: 0.8942 F1: 0.8719 Train AUC: 0.9498 Val AUC: 0.9483 Val PRC: 0.9516 Time: 0.72\n",
      "Epoch: 97 Train Loss: 0.2827 Acc: 0.8696 Pre: 0.8537 Recall: 0.8921 F1: 0.8725 Train AUC: 0.9520 Val AUC: 0.9493 Val PRC: 0.9542 Time: 0.74\n",
      "Epoch: 98 Train Loss: 0.2803 Acc: 0.8728 Pre: 0.8853 Recall: 0.8565 F1: 0.8707 Train AUC: 0.9534 Val AUC: 0.9466 Val PRC: 0.9507 Time: 0.71\n",
      "Epoch: 99 Train Loss: 0.2834 Acc: 0.8702 Pre: 0.8847 Recall: 0.8513 F1: 0.8677 Train AUC: 0.9525 Val AUC: 0.9485 Val PRC: 0.9524 Time: 0.71\n",
      "Epoch: 100 Train Loss: 0.2765 Acc: 0.8696 Pre: 0.8441 Recall: 0.9068 F1: 0.8743 Train AUC: 0.9540 Val AUC: 0.9489 Val PRC: 0.9512 Time: 0.72\n",
      "Epoch: 101 Train Loss: 0.2782 Acc: 0.8733 Pre: 0.8619 Recall: 0.8890 F1: 0.8753 Train AUC: 0.9532 Val AUC: 0.9497 Val PRC: 0.9539 Time: 0.73\n",
      "Epoch: 102 Train Loss: 0.2754 Acc: 0.8827 Pre: 0.9048 Recall: 0.8555 F1: 0.8794 Train AUC: 0.9542 Val AUC: 0.9514 Val PRC: 0.9559 Time: 0.72\n",
      "Epoch: 103 Train Loss: 0.2669 Acc: 0.8843 Pre: 0.8692 Recall: 0.9047 F1: 0.8866 Train AUC: 0.9577 Val AUC: 0.9556 Val PRC: 0.9582 Time: 0.72\n",
      "Epoch: 104 Train Loss: 0.2675 Acc: 0.8806 Pre: 0.8879 Recall: 0.8712 F1: 0.8795 Train AUC: 0.9571 Val AUC: 0.9516 Val PRC: 0.9554 Time: 0.73\n",
      "Epoch: 105 Train Loss: 0.2594 Acc: 0.8859 Pre: 0.9136 Recall: 0.8524 F1: 0.8819 Train AUC: 0.9600 Val AUC: 0.9550 Val PRC: 0.9580 Time: 0.71\n",
      "Epoch: 106 Train Loss: 0.2700 Acc: 0.8874 Pre: 0.8799 Recall: 0.8974 F1: 0.8885 Train AUC: 0.9567 Val AUC: 0.9548 Val PRC: 0.9575 Time: 0.72\n",
      "Epoch: 107 Train Loss: 0.2717 Acc: 0.8864 Pre: 0.8789 Recall: 0.8963 F1: 0.8875 Train AUC: 0.9560 Val AUC: 0.9554 Val PRC: 0.9579 Time: 0.71\n",
      "Epoch: 108 Train Loss: 0.2683 Acc: 0.8806 Pre: 0.8744 Recall: 0.8890 F1: 0.8816 Train AUC: 0.9566 Val AUC: 0.9538 Val PRC: 0.9568 Time: 0.71\n",
      "Epoch: 109 Train Loss: 0.2616 Acc: 0.8853 Pre: 0.8770 Recall: 0.8963 F1: 0.8866 Train AUC: 0.9590 Val AUC: 0.9566 Val PRC: 0.9590 Time: 0.72\n",
      "Epoch: 110 Train Loss: 0.2596 Acc: 0.8827 Pre: 0.8666 Recall: 0.9047 F1: 0.8852 Train AUC: 0.9598 Val AUC: 0.9556 Val PRC: 0.9581 Time: 0.71\n",
      "Epoch: 111 Train Loss: 0.2612 Acc: 0.8927 Pre: 0.8773 Recall: 0.9131 F1: 0.8948 Train AUC: 0.9590 Val AUC: 0.9584 Val PRC: 0.9591 Time: 0.72\n",
      "Epoch: 112 Train Loss: 0.2583 Acc: 0.8869 Pre: 0.8655 Recall: 0.9162 F1: 0.8901 Train AUC: 0.9601 Val AUC: 0.9591 Val PRC: 0.9609 Time: 0.71\n",
      "Epoch: 113 Train Loss: 0.2560 Acc: 0.8827 Pre: 0.8703 Recall: 0.8995 F1: 0.8847 Train AUC: 0.9609 Val AUC: 0.9564 Val PRC: 0.9583 Time: 0.72\n",
      "Epoch: 114 Train Loss: 0.2541 Acc: 0.8859 Pre: 0.8975 Recall: 0.8712 F1: 0.8842 Train AUC: 0.9612 Val AUC: 0.9581 Val PRC: 0.9592 Time: 0.92\n",
      "Epoch: 115 Train Loss: 0.2613 Acc: 0.8832 Pre: 0.8653 Recall: 0.9079 F1: 0.8861 Train AUC: 0.9592 Val AUC: 0.9589 Val PRC: 0.9610 Time: 0.74\n",
      "Epoch: 116 Train Loss: 0.2517 Acc: 0.8880 Pre: 0.8754 Recall: 0.9047 F1: 0.8898 Train AUC: 0.9619 Val AUC: 0.9599 Val PRC: 0.9606 Time: 0.71\n",
      "Epoch: 117 Train Loss: 0.2533 Acc: 0.8969 Pre: 0.9120 Recall: 0.8785 F1: 0.8949 Train AUC: 0.9617 Val AUC: 0.9605 Val PRC: 0.9626 Time: 0.73\n",
      "Epoch: 118 Train Loss: 0.2468 Acc: 0.8874 Pre: 0.8578 Recall: 0.9288 F1: 0.8919 Train AUC: 0.9634 Val AUC: 0.9624 Val PRC: 0.9641 Time: 0.71\n",
      "Epoch: 119 Train Loss: 0.2498 Acc: 0.8853 Pre: 0.8702 Recall: 0.9058 F1: 0.8876 Train AUC: 0.9626 Val AUC: 0.9607 Val PRC: 0.9625 Time: 0.72\n",
      "Epoch: 120 Train Loss: 0.2507 Acc: 0.8916 Pre: 0.8710 Recall: 0.9194 F1: 0.8945 Train AUC: 0.9621 Val AUC: 0.9627 Val PRC: 0.9637 Time: 0.73\n",
      "Epoch: 121 Train Loss: 0.2539 Acc: 0.8885 Pre: 0.8659 Recall: 0.9194 F1: 0.8918 Train AUC: 0.9610 Val AUC: 0.9614 Val PRC: 0.9628 Time: 0.74\n",
      "Epoch: 122 Train Loss: 0.2563 Acc: 0.8895 Pre: 0.8690 Recall: 0.9173 F1: 0.8925 Train AUC: 0.9605 Val AUC: 0.9622 Val PRC: 0.9638 Time: 0.72\n",
      "Epoch: 123 Train Loss: 0.2426 Acc: 0.8916 Pre: 0.8755 Recall: 0.9131 F1: 0.8939 Train AUC: 0.9645 Val AUC: 0.9632 Val PRC: 0.9650 Time: 0.73\n",
      "Epoch: 124 Train Loss: 0.2469 Acc: 0.8885 Pre: 0.8666 Recall: 0.9183 F1: 0.8917 Train AUC: 0.9633 Val AUC: 0.9622 Val PRC: 0.9628 Time: 0.73\n",
      "Epoch: 125 Train Loss: 0.2443 Acc: 0.8906 Pre: 0.8753 Recall: 0.9110 F1: 0.8928 Train AUC: 0.9639 Val AUC: 0.9610 Val PRC: 0.9607 Time: 0.72\n",
      "Epoch: 126 Train Loss: 0.2415 Acc: 0.8942 Pre: 0.8717 Recall: 0.9246 F1: 0.8974 Train AUC: 0.9651 Val AUC: 0.9628 Val PRC: 0.9631 Time: 0.72\n",
      "Epoch: 127 Train Loss: 0.2407 Acc: 0.8942 Pre: 0.8724 Recall: 0.9236 F1: 0.8973 Train AUC: 0.9651 Val AUC: 0.9648 Val PRC: 0.9635 Time: 0.72\n",
      "Epoch: 128 Train Loss: 0.2370 Acc: 0.8927 Pre: 0.8780 Recall: 0.9120 F1: 0.8947 Train AUC: 0.9664 Val AUC: 0.9644 Val PRC: 0.9650 Time: 0.71\n",
      "Epoch: 129 Train Loss: 0.2356 Acc: 0.8984 Pre: 0.8911 Recall: 0.9079 F1: 0.8994 Train AUC: 0.9666 Val AUC: 0.9652 Val PRC: 0.9662 Time: 0.72\n",
      "Epoch: 130 Train Loss: 0.2318 Acc: 0.8942 Pre: 0.8862 Recall: 0.9047 F1: 0.8953 Train AUC: 0.9677 Val AUC: 0.9648 Val PRC: 0.9658 Time: 0.72\n",
      "Epoch: 131 Train Loss: 0.2368 Acc: 0.8958 Pre: 0.8826 Recall: 0.9131 F1: 0.8976 Train AUC: 0.9660 Val AUC: 0.9647 Val PRC: 0.9658 Time: 0.73\n",
      "Epoch: 132 Train Loss: 0.2423 Acc: 0.8927 Pre: 0.8811 Recall: 0.9079 F1: 0.8943 Train AUC: 0.9644 Val AUC: 0.9641 Val PRC: 0.9636 Time: 0.71\n",
      "Epoch: 133 Train Loss: 0.2449 Acc: 0.8979 Pre: 0.9060 Recall: 0.8880 F1: 0.8969 Train AUC: 0.9640 Val AUC: 0.9643 Val PRC: 0.9641 Time: 0.71\n",
      "Epoch: 134 Train Loss: 0.2371 Acc: 0.8995 Pre: 0.9020 Recall: 0.8963 F1: 0.8992 Train AUC: 0.9659 Val AUC: 0.9664 Val PRC: 0.9674 Time: 0.71\n",
      "Epoch: 135 Train Loss: 0.2340 Acc: 0.8979 Pre: 0.9060 Recall: 0.8880 F1: 0.8969 Train AUC: 0.9666 Val AUC: 0.9656 Val PRC: 0.9653 Time: 0.71\n",
      "Epoch: 136 Train Loss: 0.2389 Acc: 0.8974 Pre: 0.8941 Recall: 0.9016 F1: 0.8978 Train AUC: 0.9655 Val AUC: 0.9665 Val PRC: 0.9661 Time: 0.72\n",
      "Epoch: 137 Train Loss: 0.2403 Acc: 0.8990 Pre: 0.8735 Recall: 0.9330 F1: 0.9023 Train AUC: 0.9647 Val AUC: 0.9667 Val PRC: 0.9662 Time: 0.72\n",
      "Epoch: 138 Train Loss: 0.2272 Acc: 0.8937 Pre: 0.8615 Recall: 0.9382 F1: 0.8982 Train AUC: 0.9689 Val AUC: 0.9677 Val PRC: 0.9684 Time: 0.72\n",
      "Epoch: 139 Train Loss: 0.2296 Acc: 0.8963 Pre: 0.8729 Recall: 0.9277 F1: 0.8995 Train AUC: 0.9677 Val AUC: 0.9682 Val PRC: 0.9680 Time: 0.69\n",
      "Epoch: 140 Train Loss: 0.2280 Acc: 0.8974 Pre: 0.8754 Recall: 0.9267 F1: 0.9003 Train AUC: 0.9684 Val AUC: 0.9651 Val PRC: 0.9611 Time: 0.75\n",
      "Epoch: 141 Train Loss: 0.2293 Acc: 0.8974 Pre: 0.8806 Recall: 0.9194 F1: 0.8996 Train AUC: 0.9679 Val AUC: 0.9658 Val PRC: 0.9663 Time: 0.72\n",
      "Epoch: 142 Train Loss: 0.2420 Acc: 0.9005 Pre: 0.8923 Recall: 0.9110 F1: 0.9016 Train AUC: 0.9639 Val AUC: 0.9662 Val PRC: 0.9654 Time: 0.72\n",
      "Epoch: 143 Train Loss: 0.2280 Acc: 0.9016 Pre: 0.9033 Recall: 0.8995 F1: 0.9014 Train AUC: 0.9686 Val AUC: 0.9670 Val PRC: 0.9674 Time: 0.71\n",
      "Epoch: 144 Train Loss: 0.2212 Acc: 0.9016 Pre: 0.8999 Recall: 0.9037 F1: 0.9018 Train AUC: 0.9707 Val AUC: 0.9664 Val PRC: 0.9633 Time: 0.73\n",
      "Epoch: 145 Train Loss: 0.2254 Acc: 0.8995 Pre: 0.8881 Recall: 0.9141 F1: 0.9009 Train AUC: 0.9689 Val AUC: 0.9675 Val PRC: 0.9676 Time: 0.72\n",
      "Epoch: 146 Train Loss: 0.2251 Acc: 0.9026 Pre: 0.8880 Recall: 0.9215 F1: 0.9044 Train AUC: 0.9690 Val AUC: 0.9670 Val PRC: 0.9654 Time: 0.72\n",
      "Epoch: 147 Train Loss: 0.2258 Acc: 0.8979 Pre: 0.8696 Recall: 0.9361 F1: 0.9017 Train AUC: 0.9687 Val AUC: 0.9668 Val PRC: 0.9659 Time: 0.71\n",
      "Epoch: 148 Train Loss: 0.2267 Acc: 0.9052 Pre: 0.9329 Recall: 0.8733 F1: 0.9021 Train AUC: 0.9687 Val AUC: 0.9681 Val PRC: 0.9684 Time: 0.72\n",
      "Epoch: 149 Train Loss: 0.2304 Acc: 0.8963 Pre: 0.8766 Recall: 0.9225 F1: 0.8990 Train AUC: 0.9676 Val AUC: 0.9658 Val PRC: 0.9660 Time: 0.73\n",
      "Epoch: 150 Train Loss: 0.2215 Acc: 0.8974 Pre: 0.8653 Recall: 0.9414 F1: 0.9017 Train AUC: 0.9697 Val AUC: 0.9710 Val PRC: 0.9713 Time: 0.75\n",
      "Epoch: 151 Train Loss: 0.2242 Acc: 0.9000 Pre: 0.8752 Recall: 0.9330 F1: 0.9032 Train AUC: 0.9692 Val AUC: 0.9686 Val PRC: 0.9674 Time: 0.71\n",
      "Epoch: 152 Train Loss: 0.2160 Acc: 0.9031 Pre: 0.8865 Recall: 0.9246 F1: 0.9052 Train AUC: 0.9714 Val AUC: 0.9683 Val PRC: 0.9672 Time: 0.74\n",
      "Epoch: 153 Train Loss: 0.2308 Acc: 0.9016 Pre: 0.9093 Recall: 0.8921 F1: 0.9006 Train AUC: 0.9681 Val AUC: 0.9686 Val PRC: 0.9689 Time: 0.72\n",
      "Epoch: 154 Train Loss: 0.2232 Acc: 0.9021 Pre: 0.8871 Recall: 0.9215 F1: 0.9040 Train AUC: 0.9697 Val AUC: 0.9702 Val PRC: 0.9697 Time: 0.72\n",
      "Epoch: 155 Train Loss: 0.2122 Acc: 0.9047 Pre: 0.8940 Recall: 0.9183 F1: 0.9060 Train AUC: 0.9727 Val AUC: 0.9681 Val PRC: 0.9681 Time: 0.72\n",
      "Epoch: 156 Train Loss: 0.2206 Acc: 0.9042 Pre: 0.9080 Recall: 0.8995 F1: 0.9037 Train AUC: 0.9703 Val AUC: 0.9686 Val PRC: 0.9676 Time: 0.70\n",
      "Epoch: 157 Train Loss: 0.2171 Acc: 0.9000 Pre: 0.8659 Recall: 0.9466 F1: 0.9045 Train AUC: 0.9710 Val AUC: 0.9697 Val PRC: 0.9689 Time: 0.71\n",
      "Epoch: 158 Train Loss: 0.2202 Acc: 0.9073 Pre: 0.8890 Recall: 0.9309 F1: 0.9095 Train AUC: 0.9706 Val AUC: 0.9697 Val PRC: 0.9673 Time: 0.72\n",
      "Epoch: 159 Train Loss: 0.2217 Acc: 0.9058 Pre: 0.8871 Recall: 0.9298 F1: 0.9080 Train AUC: 0.9695 Val AUC: 0.9693 Val PRC: 0.9657 Time: 0.72\n",
      "Epoch: 160 Train Loss: 0.2136 Acc: 0.9037 Pre: 0.8922 Recall: 0.9183 F1: 0.9051 Train AUC: 0.9717 Val AUC: 0.9695 Val PRC: 0.9686 Time: 0.71\n",
      "Epoch: 161 Train Loss: 0.2314 Acc: 0.9052 Pre: 0.8893 Recall: 0.9257 F1: 0.9071 Train AUC: 0.9682 Val AUC: 0.9694 Val PRC: 0.9689 Time: 0.72\n",
      "Epoch: 162 Train Loss: 0.2155 Acc: 0.9068 Pre: 0.9370 Recall: 0.8723 F1: 0.9035 Train AUC: 0.9710 Val AUC: 0.9681 Val PRC: 0.9620 Time: 0.72\n",
      "Epoch: 163 Train Loss: 0.2056 Acc: 0.9058 Pre: 0.9109 Recall: 0.8995 F1: 0.9052 Train AUC: 0.9741 Val AUC: 0.9694 Val PRC: 0.9689 Time: 0.71\n",
      "Epoch: 164 Train Loss: 0.2014 Acc: 0.9099 Pre: 0.8959 Recall: 0.9277 F1: 0.9115 Train AUC: 0.9751 Val AUC: 0.9704 Val PRC: 0.9706 Time: 0.72\n",
      "Epoch: 165 Train Loss: 0.2169 Acc: 0.9084 Pre: 0.8846 Recall: 0.9393 F1: 0.9111 Train AUC: 0.9714 Val AUC: 0.9712 Val PRC: 0.9708 Time: 0.71\n",
      "Epoch: 166 Train Loss: 0.2125 Acc: 0.9110 Pre: 0.9084 Recall: 0.9141 F1: 0.9113 Train AUC: 0.9722 Val AUC: 0.9720 Val PRC: 0.9718 Time: 0.71\n",
      "Epoch: 167 Train Loss: 0.2126 Acc: 0.9131 Pre: 0.9071 Recall: 0.9204 F1: 0.9137 Train AUC: 0.9719 Val AUC: 0.9722 Val PRC: 0.9721 Time: 0.70\n",
      "Epoch: 168 Train Loss: 0.2115 Acc: 0.9089 Pre: 0.8956 Recall: 0.9257 F1: 0.9104 Train AUC: 0.9725 Val AUC: 0.9714 Val PRC: 0.9709 Time: 0.71\n",
      "Epoch: 169 Train Loss: 0.2030 Acc: 0.9120 Pre: 0.9173 Recall: 0.9058 F1: 0.9115 Train AUC: 0.9750 Val AUC: 0.9739 Val PRC: 0.9737 Time: 0.71\n",
      "Epoch: 170 Train Loss: 0.1990 Acc: 0.9058 Pre: 0.8825 Recall: 0.9361 F1: 0.9085 Train AUC: 0.9761 Val AUC: 0.9713 Val PRC: 0.9710 Time: 0.71\n",
      "Epoch: 171 Train Loss: 0.2051 Acc: 0.9168 Pre: 0.9189 Recall: 0.9141 F1: 0.9165 Train AUC: 0.9739 Val AUC: 0.9728 Val PRC: 0.9713 Time: 0.72\n",
      "Epoch: 172 Train Loss: 0.1959 Acc: 0.9147 Pre: 0.9091 Recall: 0.9215 F1: 0.9152 Train AUC: 0.9768 Val AUC: 0.9720 Val PRC: 0.9716 Time: 0.71\n",
      "Epoch: 173 Train Loss: 0.2057 Acc: 0.9068 Pre: 0.8813 Recall: 0.9403 F1: 0.9098 Train AUC: 0.9740 Val AUC: 0.9718 Val PRC: 0.9705 Time: 0.72\n",
      "Epoch: 174 Train Loss: 0.2103 Acc: 0.9105 Pre: 0.9126 Recall: 0.9079 F1: 0.9102 Train AUC: 0.9730 Val AUC: 0.9715 Val PRC: 0.9711 Time: 0.73\n",
      "Epoch: 175 Train Loss: 0.2003 Acc: 0.9094 Pre: 0.8957 Recall: 0.9267 F1: 0.9110 Train AUC: 0.9752 Val AUC: 0.9728 Val PRC: 0.9725 Time: 0.71\n",
      "Epoch: 176 Train Loss: 0.2082 Acc: 0.9136 Pre: 0.8990 Recall: 0.9319 F1: 0.9152 Train AUC: 0.9731 Val AUC: 0.9726 Val PRC: 0.9720 Time: 0.69\n",
      "Epoch: 177 Train Loss: 0.1925 Acc: 0.9068 Pre: 0.8889 Recall: 0.9298 F1: 0.9089 Train AUC: 0.9772 Val AUC: 0.9719 Val PRC: 0.9714 Time: 0.72\n",
      "Epoch: 178 Train Loss: 0.2093 Acc: 0.9199 Pre: 0.8994 Recall: 0.9455 F1: 0.9219 Train AUC: 0.9729 Val AUC: 0.9751 Val PRC: 0.9723 Time: 0.72\n",
      "Epoch: 179 Train Loss: 0.1999 Acc: 0.9120 Pre: 0.9227 Recall: 0.8995 F1: 0.9109 Train AUC: 0.9751 Val AUC: 0.9733 Val PRC: 0.9730 Time: 0.72\n",
      "Epoch: 180 Train Loss: 0.1966 Acc: 0.9120 Pre: 0.8923 Recall: 0.9372 F1: 0.9142 Train AUC: 0.9760 Val AUC: 0.9744 Val PRC: 0.9721 Time: 0.74\n",
      "Epoch: 181 Train Loss: 0.1899 Acc: 0.9110 Pre: 0.8929 Recall: 0.9340 F1: 0.9130 Train AUC: 0.9778 Val AUC: 0.9742 Val PRC: 0.9739 Time: 0.73\n",
      "Epoch: 182 Train Loss: 0.1914 Acc: 0.9141 Pre: 0.8991 Recall: 0.9330 F1: 0.9157 Train AUC: 0.9773 Val AUC: 0.9751 Val PRC: 0.9756 Time: 0.72\n",
      "Epoch: 183 Train Loss: 0.1910 Acc: 0.9194 Pre: 0.9274 Recall: 0.9099 F1: 0.9186 Train AUC: 0.9777 Val AUC: 0.9740 Val PRC: 0.9727 Time: 0.73\n",
      "Epoch: 184 Train Loss: 0.1968 Acc: 0.9147 Pre: 0.9041 Recall: 0.9277 F1: 0.9158 Train AUC: 0.9760 Val AUC: 0.9729 Val PRC: 0.9699 Time: 0.71\n",
      "Epoch: 185 Train Loss: 0.1951 Acc: 0.9173 Pre: 0.9079 Recall: 0.9288 F1: 0.9182 Train AUC: 0.9766 Val AUC: 0.9757 Val PRC: 0.9749 Time: 0.71\n",
      "Epoch: 186 Train Loss: 0.1911 Acc: 0.9204 Pre: 0.9294 Recall: 0.9099 F1: 0.9196 Train AUC: 0.9772 Val AUC: 0.9756 Val PRC: 0.9752 Time: 0.72\n",
      "Epoch: 187 Train Loss: 0.1958 Acc: 0.9126 Pre: 0.8917 Recall: 0.9393 F1: 0.9148 Train AUC: 0.9761 Val AUC: 0.9724 Val PRC: 0.9719 Time: 0.72\n",
      "Epoch: 188 Train Loss: 0.1940 Acc: 0.9194 Pre: 0.9125 Recall: 0.9277 F1: 0.9200 Train AUC: 0.9765 Val AUC: 0.9742 Val PRC: 0.9744 Time: 0.70\n",
      "Epoch: 189 Train Loss: 0.2081 Acc: 0.9168 Pre: 0.9207 Recall: 0.9120 F1: 0.9164 Train AUC: 0.9723 Val AUC: 0.9758 Val PRC: 0.9753 Time: 0.72\n",
      "Epoch: 190 Train Loss: 0.1958 Acc: 0.9168 Pre: 0.9243 Recall: 0.9079 F1: 0.9160 Train AUC: 0.9757 Val AUC: 0.9744 Val PRC: 0.9735 Time: 0.71\n",
      "Epoch: 191 Train Loss: 0.1878 Acc: 0.9188 Pre: 0.9301 Recall: 0.9058 F1: 0.9178 Train AUC: 0.9779 Val AUC: 0.9732 Val PRC: 0.9727 Time: 0.70\n",
      "Epoch: 192 Train Loss: 0.1890 Acc: 0.9152 Pre: 0.8961 Recall: 0.9393 F1: 0.9172 Train AUC: 0.9777 Val AUC: 0.9738 Val PRC: 0.9730 Time: 0.72\n",
      "Epoch: 193 Train Loss: 0.2017 Acc: 0.9136 Pre: 0.8990 Recall: 0.9319 F1: 0.9152 Train AUC: 0.9749 Val AUC: 0.9727 Val PRC: 0.9730 Time: 0.71\n",
      "Epoch: 194 Train Loss: 0.1923 Acc: 0.9157 Pre: 0.9170 Recall: 0.9141 F1: 0.9156 Train AUC: 0.9767 Val AUC: 0.9729 Val PRC: 0.9720 Time: 0.73\n",
      "Epoch: 195 Train Loss: 0.1969 Acc: 0.9152 Pre: 0.9109 Recall: 0.9204 F1: 0.9156 Train AUC: 0.9756 Val AUC: 0.9731 Val PRC: 0.9727 Time: 0.73\n",
      "Epoch: 196 Train Loss: 0.1882 Acc: 0.9173 Pre: 0.9121 Recall: 0.9236 F1: 0.9178 Train AUC: 0.9778 Val AUC: 0.9730 Val PRC: 0.9721 Time: 0.72\n",
      "Epoch: 197 Train Loss: 0.1903 Acc: 0.9147 Pre: 0.8960 Recall: 0.9382 F1: 0.9166 Train AUC: 0.9771 Val AUC: 0.9718 Val PRC: 0.9679 Time: 0.72\n",
      "Epoch: 198 Train Loss: 0.1921 Acc: 0.9162 Pre: 0.9069 Recall: 0.9277 F1: 0.9172 Train AUC: 0.9774 Val AUC: 0.9734 Val PRC: 0.9728 Time: 0.74\n",
      "Epoch: 199 Train Loss: 0.1887 Acc: 0.9131 Pre: 0.9080 Recall: 0.9194 F1: 0.9136 Train AUC: 0.9784 Val AUC: 0.9736 Val PRC: 0.9737 Time: 0.72\n",
      "Epoch: 200 Train Loss: 0.1925 Acc: 0.9194 Pre: 0.9017 Recall: 0.9414 F1: 0.9211 Train AUC: 0.9767 Val AUC: 0.9755 Val PRC: 0.9748 Time: 0.71\n",
      "Epoch: 201 Train Loss: 0.1931 Acc: 0.9173 Pre: 0.9346 Recall: 0.8974 F1: 0.9156 Train AUC: 0.9769 Val AUC: 0.9749 Val PRC: 0.9749 Time: 0.74\n",
      "Epoch: 202 Train Loss: 0.1808 Acc: 0.9194 Pre: 0.9283 Recall: 0.9089 F1: 0.9185 Train AUC: 0.9799 Val AUC: 0.9751 Val PRC: 0.9753 Time: 0.72\n",
      "Epoch: 203 Train Loss: 0.1845 Acc: 0.9147 Pre: 0.9008 Recall: 0.9319 F1: 0.9161 Train AUC: 0.9788 Val AUC: 0.9754 Val PRC: 0.9759 Time: 0.73\n",
      "Epoch: 204 Train Loss: 0.1829 Acc: 0.9147 Pre: 0.8929 Recall: 0.9424 F1: 0.9170 Train AUC: 0.9790 Val AUC: 0.9749 Val PRC: 0.9745 Time: 0.73\n",
      "Epoch: 205 Train Loss: 0.1880 Acc: 0.9183 Pre: 0.8913 Recall: 0.9529 F1: 0.9211 Train AUC: 0.9779 Val AUC: 0.9755 Val PRC: 0.9747 Time: 0.71\n",
      "Epoch: 206 Train Loss: 0.1789 Acc: 0.9199 Pre: 0.9059 Recall: 0.9372 F1: 0.9213 Train AUC: 0.9797 Val AUC: 0.9748 Val PRC: 0.9738 Time: 0.72\n",
      "Epoch: 207 Train Loss: 0.1901 Acc: 0.9204 Pre: 0.9003 Recall: 0.9455 F1: 0.9224 Train AUC: 0.9767 Val AUC: 0.9737 Val PRC: 0.9724 Time: 0.74\n",
      "Epoch: 208 Train Loss: 0.1749 Acc: 0.9173 Pre: 0.8888 Recall: 0.9539 F1: 0.9202 Train AUC: 0.9809 Val AUC: 0.9752 Val PRC: 0.9740 Time: 0.72\n",
      "Epoch: 209 Train Loss: 0.1845 Acc: 0.9241 Pre: 0.9327 Recall: 0.9141 F1: 0.9233 Train AUC: 0.9782 Val AUC: 0.9756 Val PRC: 0.9737 Time: 0.73\n",
      "Epoch: 210 Train Loss: 0.1759 Acc: 0.9251 Pre: 0.9134 Recall: 0.9393 F1: 0.9262 Train AUC: 0.9802 Val AUC: 0.9767 Val PRC: 0.9758 Time: 0.75\n",
      "Epoch: 211 Train Loss: 0.1753 Acc: 0.9173 Pre: 0.9112 Recall: 0.9246 F1: 0.9179 Train AUC: 0.9801 Val AUC: 0.9743 Val PRC: 0.9717 Time: 0.72\n",
      "Epoch: 212 Train Loss: 0.1830 Acc: 0.9178 Pre: 0.9218 Recall: 0.9131 F1: 0.9174 Train AUC: 0.9787 Val AUC: 0.9766 Val PRC: 0.9768 Time: 0.73\n",
      "Epoch: 213 Train Loss: 0.1794 Acc: 0.9236 Pre: 0.9107 Recall: 0.9393 F1: 0.9247 Train AUC: 0.9795 Val AUC: 0.9765 Val PRC: 0.9751 Time: 0.73\n",
      "Epoch: 214 Train Loss: 0.1683 Acc: 0.9220 Pre: 0.9087 Recall: 0.9382 F1: 0.9232 Train AUC: 0.9822 Val AUC: 0.9773 Val PRC: 0.9776 Time: 0.72\n",
      "Epoch: 215 Train Loss: 0.1779 Acc: 0.9183 Pre: 0.9048 Recall: 0.9351 F1: 0.9197 Train AUC: 0.9801 Val AUC: 0.9768 Val PRC: 0.9763 Time: 0.71\n",
      "Epoch: 216 Train Loss: 0.1767 Acc: 0.9246 Pre: 0.9228 Recall: 0.9267 F1: 0.9248 Train AUC: 0.9803 Val AUC: 0.9785 Val PRC: 0.9776 Time: 0.72\n",
      "Epoch: 217 Train Loss: 0.1841 Acc: 0.9168 Pre: 0.8879 Recall: 0.9539 F1: 0.9197 Train AUC: 0.9785 Val AUC: 0.9762 Val PRC: 0.9751 Time: 0.71\n",
      "Epoch: 218 Train Loss: 0.1827 Acc: 0.9183 Pre: 0.9114 Recall: 0.9267 F1: 0.9190 Train AUC: 0.9787 Val AUC: 0.9752 Val PRC: 0.9748 Time: 0.71\n",
      "Epoch: 219 Train Loss: 0.1753 Acc: 0.9209 Pre: 0.9153 Recall: 0.9277 F1: 0.9215 Train AUC: 0.9805 Val AUC: 0.9781 Val PRC: 0.9778 Time: 0.72\n",
      "Epoch: 220 Train Loss: 0.1864 Acc: 0.9173 Pre: 0.9087 Recall: 0.9277 F1: 0.9181 Train AUC: 0.9779 Val AUC: 0.9755 Val PRC: 0.9746 Time: 0.72\n",
      "Epoch: 221 Train Loss: 0.1671 Acc: 0.9157 Pre: 0.8915 Recall: 0.9466 F1: 0.9182 Train AUC: 0.9823 Val AUC: 0.9756 Val PRC: 0.9750 Time: 0.71\n",
      "Epoch: 222 Train Loss: 0.1767 Acc: 0.9152 Pre: 0.9067 Recall: 0.9257 F1: 0.9161 Train AUC: 0.9801 Val AUC: 0.9760 Val PRC: 0.9754 Time: 0.73\n",
      "Epoch: 223 Train Loss: 0.1788 Acc: 0.9152 Pre: 0.8945 Recall: 0.9414 F1: 0.9173 Train AUC: 0.9792 Val AUC: 0.9766 Val PRC: 0.9765 Time: 0.71\n",
      "Epoch: 224 Train Loss: 0.1704 Acc: 0.9257 Pre: 0.9161 Recall: 0.9372 F1: 0.9265 Train AUC: 0.9813 Val AUC: 0.9791 Val PRC: 0.9785 Time: 0.70\n",
      "Epoch: 225 Train Loss: 0.1649 Acc: 0.9230 Pre: 0.9217 Recall: 0.9246 F1: 0.9232 Train AUC: 0.9826 Val AUC: 0.9771 Val PRC: 0.9765 Time: 0.72\n",
      "Epoch: 226 Train Loss: 0.1747 Acc: 0.9152 Pre: 0.8853 Recall: 0.9539 F1: 0.9183 Train AUC: 0.9795 Val AUC: 0.9751 Val PRC: 0.9738 Time: 0.73\n",
      "Epoch: 227 Train Loss: 0.1635 Acc: 0.9215 Pre: 0.9070 Recall: 0.9393 F1: 0.9228 Train AUC: 0.9830 Val AUC: 0.9770 Val PRC: 0.9766 Time: 0.72\n",
      "Epoch: 228 Train Loss: 0.1728 Acc: 0.9204 Pre: 0.8879 Recall: 0.9623 F1: 0.9236 Train AUC: 0.9812 Val AUC: 0.9777 Val PRC: 0.9777 Time: 0.71\n",
      "Epoch: 229 Train Loss: 0.1657 Acc: 0.9267 Pre: 0.9171 Recall: 0.9382 F1: 0.9275 Train AUC: 0.9827 Val AUC: 0.9773 Val PRC: 0.9770 Time: 0.72\n",
      "Epoch: 230 Train Loss: 0.1665 Acc: 0.9183 Pre: 0.8898 Recall: 0.9550 F1: 0.9212 Train AUC: 0.9820 Val AUC: 0.9764 Val PRC: 0.9766 Time: 0.71\n",
      "Epoch: 231 Train Loss: 0.1729 Acc: 0.9204 Pre: 0.9068 Recall: 0.9372 F1: 0.9217 Train AUC: 0.9812 Val AUC: 0.9764 Val PRC: 0.9765 Time: 0.73\n",
      "Epoch: 232 Train Loss: 0.1721 Acc: 0.9272 Pre: 0.9180 Recall: 0.9382 F1: 0.9280 Train AUC: 0.9811 Val AUC: 0.9778 Val PRC: 0.9775 Time: 0.74\n",
      "Epoch: 233 Train Loss: 0.1613 Acc: 0.9230 Pre: 0.9156 Recall: 0.9319 F1: 0.9237 Train AUC: 0.9839 Val AUC: 0.9778 Val PRC: 0.9781 Time: 0.71\n",
      "Epoch: 234 Train Loss: 0.1673 Acc: 0.9230 Pre: 0.9032 Recall: 0.9476 F1: 0.9249 Train AUC: 0.9822 Val AUC: 0.9786 Val PRC: 0.9797 Time: 0.73\n",
      "Epoch: 235 Train Loss: 0.1760 Acc: 0.9246 Pre: 0.9125 Recall: 0.9393 F1: 0.9257 Train AUC: 0.9798 Val AUC: 0.9773 Val PRC: 0.9772 Time: 0.73\n",
      "Epoch: 236 Train Loss: 0.1681 Acc: 0.9236 Pre: 0.9025 Recall: 0.9497 F1: 0.9255 Train AUC: 0.9814 Val AUC: 0.9766 Val PRC: 0.9722 Time: 0.72\n",
      "Epoch: 237 Train Loss: 0.1694 Acc: 0.9220 Pre: 0.9022 Recall: 0.9466 F1: 0.9239 Train AUC: 0.9817 Val AUC: 0.9778 Val PRC: 0.9783 Time: 0.72\n",
      "Epoch: 238 Train Loss: 0.1628 Acc: 0.9209 Pre: 0.9127 Recall: 0.9309 F1: 0.9217 Train AUC: 0.9828 Val AUC: 0.9786 Val PRC: 0.9778 Time: 0.74\n",
      "Epoch: 239 Train Loss: 0.1681 Acc: 0.9277 Pre: 0.9207 Recall: 0.9361 F1: 0.9283 Train AUC: 0.9813 Val AUC: 0.9804 Val PRC: 0.9806 Time: 0.72\n",
      "Epoch: 240 Train Loss: 0.1536 Acc: 0.9272 Pre: 0.9155 Recall: 0.9414 F1: 0.9282 Train AUC: 0.9845 Val AUC: 0.9795 Val PRC: 0.9795 Time: 0.72\n",
      "Epoch: 241 Train Loss: 0.1634 Acc: 0.9267 Pre: 0.9267 Recall: 0.9267 F1: 0.9267 Train AUC: 0.9830 Val AUC: 0.9780 Val PRC: 0.9781 Time: 0.74\n",
      "Epoch: 242 Train Loss: 0.1510 Acc: 0.9241 Pre: 0.9412 Recall: 0.9047 F1: 0.9226 Train AUC: 0.9855 Val AUC: 0.9784 Val PRC: 0.9791 Time: 0.74\n",
      "Epoch: 243 Train Loss: 0.1699 Acc: 0.9246 Pre: 0.9108 Recall: 0.9414 F1: 0.9258 Train AUC: 0.9811 Val AUC: 0.9777 Val PRC: 0.9778 Time: 0.72\n",
      "Epoch: 244 Train Loss: 0.1574 Acc: 0.9257 Pre: 0.9293 Recall: 0.9215 F1: 0.9253 Train AUC: 0.9836 Val AUC: 0.9781 Val PRC: 0.9786 Time: 0.73\n",
      "Epoch: 245 Train Loss: 0.1578 Acc: 0.9236 Pre: 0.9174 Recall: 0.9309 F1: 0.9241 Train AUC: 0.9836 Val AUC: 0.9786 Val PRC: 0.9787 Time: 0.71\n",
      "Epoch: 246 Train Loss: 0.1618 Acc: 0.9262 Pre: 0.9205 Recall: 0.9330 F1: 0.9267 Train AUC: 0.9828 Val AUC: 0.9790 Val PRC: 0.9792 Time: 0.73\n",
      "Epoch: 247 Train Loss: 0.1498 Acc: 0.9272 Pre: 0.9313 Recall: 0.9225 F1: 0.9269 Train AUC: 0.9854 Val AUC: 0.9790 Val PRC: 0.9782 Time: 0.94\n",
      "Epoch: 248 Train Loss: 0.1580 Acc: 0.9220 Pre: 0.9233 Recall: 0.9204 F1: 0.9219 Train AUC: 0.9836 Val AUC: 0.9763 Val PRC: 0.9765 Time: 0.72\n",
      "Epoch: 249 Train Loss: 0.1490 Acc: 0.9215 Pre: 0.8942 Recall: 0.9560 F1: 0.9241 Train AUC: 0.9852 Val AUC: 0.9767 Val PRC: 0.9736 Time: 0.73\n",
      "Epoch: 250 Train Loss: 0.1528 Acc: 0.9288 Pre: 0.9166 Recall: 0.9435 F1: 0.9298 Train AUC: 0.9847 Val AUC: 0.9795 Val PRC: 0.9796 Time: 0.74\n",
      "Epoch: 251 Train Loss: 0.1556 Acc: 0.9236 Pre: 0.9074 Recall: 0.9435 F1: 0.9251 Train AUC: 0.9839 Val AUC: 0.9778 Val PRC: 0.9782 Time: 0.71\n",
      "Epoch: 252 Train Loss: 0.1608 Acc: 0.9236 Pre: 0.9157 Recall: 0.9330 F1: 0.9243 Train AUC: 0.9828 Val AUC: 0.9772 Val PRC: 0.9775 Time: 0.71\n",
      "Epoch: 253 Train Loss: 0.1593 Acc: 0.9257 Pre: 0.9169 Recall: 0.9361 F1: 0.9264 Train AUC: 0.9830 Val AUC: 0.9777 Val PRC: 0.9753 Time: 0.73\n",
      "Epoch: 254 Train Loss: 0.1498 Acc: 0.9335 Pre: 0.9312 Recall: 0.9361 F1: 0.9337 Train AUC: 0.9851 Val AUC: 0.9787 Val PRC: 0.9776 Time: 0.71\n",
      "Epoch: 255 Train Loss: 0.1602 Acc: 0.9272 Pre: 0.9277 Recall: 0.9267 F1: 0.9272 Train AUC: 0.9826 Val AUC: 0.9796 Val PRC: 0.9796 Time: 0.71\n",
      "Epoch: 256 Train Loss: 0.1509 Acc: 0.9288 Pre: 0.9226 Recall: 0.9361 F1: 0.9293 Train AUC: 0.9849 Val AUC: 0.9779 Val PRC: 0.9775 Time: 0.74\n",
      "Epoch: 257 Train Loss: 0.1501 Acc: 0.9314 Pre: 0.9274 Recall: 0.9361 F1: 0.9317 Train AUC: 0.9847 Val AUC: 0.9773 Val PRC: 0.9728 Time: 0.72\n",
      "Epoch: 258 Train Loss: 0.1487 Acc: 0.9267 Pre: 0.9039 Recall: 0.9550 F1: 0.9287 Train AUC: 0.9856 Val AUC: 0.9784 Val PRC: 0.9769 Time: 0.72\n",
      "Epoch: 259 Train Loss: 0.1570 Acc: 0.9272 Pre: 0.9105 Recall: 0.9476 F1: 0.9287 Train AUC: 0.9836 Val AUC: 0.9770 Val PRC: 0.9734 Time: 0.74\n",
      "Epoch: 260 Train Loss: 0.1461 Acc: 0.9241 Pre: 0.9050 Recall: 0.9476 F1: 0.9258 Train AUC: 0.9862 Val AUC: 0.9787 Val PRC: 0.9794 Time: 0.72\n",
      "Epoch: 261 Train Loss: 0.1431 Acc: 0.9225 Pre: 0.9088 Recall: 0.9393 F1: 0.9238 Train AUC: 0.9868 Val AUC: 0.9782 Val PRC: 0.9779 Time: 0.73\n",
      "Epoch: 262 Train Loss: 0.1576 Acc: 0.9230 Pre: 0.9131 Recall: 0.9351 F1: 0.9240 Train AUC: 0.9832 Val AUC: 0.9788 Val PRC: 0.9798 Time: 0.73\n",
      "Epoch: 263 Train Loss: 0.1533 Acc: 0.9241 Pre: 0.9327 Recall: 0.9141 F1: 0.9233 Train AUC: 0.9851 Val AUC: 0.9780 Val PRC: 0.9785 Time: 0.73\n",
      "Epoch: 264 Train Loss: 0.1431 Acc: 0.9277 Pre: 0.9295 Recall: 0.9257 F1: 0.9276 Train AUC: 0.9866 Val AUC: 0.9794 Val PRC: 0.9799 Time: 0.72\n",
      "Epoch: 265 Train Loss: 0.1496 Acc: 0.9293 Pre: 0.9343 Recall: 0.9236 F1: 0.9289 Train AUC: 0.9854 Val AUC: 0.9808 Val PRC: 0.9804 Time: 0.74\n",
      "Epoch: 266 Train Loss: 0.1431 Acc: 0.9236 Pre: 0.8970 Recall: 0.9571 F1: 0.9260 Train AUC: 0.9864 Val AUC: 0.9794 Val PRC: 0.9794 Time: 0.71\n",
      "Epoch: 267 Train Loss: 0.1567 Acc: 0.9298 Pre: 0.9210 Recall: 0.9403 F1: 0.9306 Train AUC: 0.9832 Val AUC: 0.9785 Val PRC: 0.9759 Time: 0.74\n",
      "Epoch: 268 Train Loss: 0.1489 Acc: 0.9267 Pre: 0.9015 Recall: 0.9581 F1: 0.9289 Train AUC: 0.9854 Val AUC: 0.9801 Val PRC: 0.9812 Time: 0.73\n",
      "Epoch: 269 Train Loss: 0.1483 Acc: 0.9283 Pre: 0.9243 Recall: 0.9330 F1: 0.9286 Train AUC: 0.9844 Val AUC: 0.9804 Val PRC: 0.9806 Time: 0.72\n",
      "Epoch: 270 Train Loss: 0.1503 Acc: 0.9277 Pre: 0.9139 Recall: 0.9445 F1: 0.9289 Train AUC: 0.9850 Val AUC: 0.9781 Val PRC: 0.9767 Time: 0.72\n",
      "Epoch: 271 Train Loss: 0.1556 Acc: 0.9257 Pre: 0.9085 Recall: 0.9466 F1: 0.9272 Train AUC: 0.9833 Val AUC: 0.9796 Val PRC: 0.9799 Time: 0.75\n",
      "Epoch: 272 Train Loss: 0.1573 Acc: 0.9346 Pre: 0.9261 Recall: 0.9445 F1: 0.9352 Train AUC: 0.9835 Val AUC: 0.9806 Val PRC: 0.9812 Time: 0.71\n",
      "Epoch: 273 Train Loss: 0.1450 Acc: 0.9293 Pre: 0.9167 Recall: 0.9445 F1: 0.9304 Train AUC: 0.9859 Val AUC: 0.9802 Val PRC: 0.9796 Time: 0.72\n",
      "Epoch: 274 Train Loss: 0.1399 Acc: 0.9298 Pre: 0.9219 Recall: 0.9393 F1: 0.9305 Train AUC: 0.9864 Val AUC: 0.9802 Val PRC: 0.9799 Time: 0.72\n",
      "Epoch: 275 Train Loss: 0.1363 Acc: 0.9272 Pre: 0.9048 Recall: 0.9550 F1: 0.9292 Train AUC: 0.9871 Val AUC: 0.9794 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 276 Train Loss: 0.1495 Acc: 0.9304 Pre: 0.9354 Recall: 0.9246 F1: 0.9300 Train AUC: 0.9850 Val AUC: 0.9816 Val PRC: 0.9815 Time: 0.71\n",
      "Epoch: 277 Train Loss: 0.1475 Acc: 0.9293 Pre: 0.9316 Recall: 0.9267 F1: 0.9291 Train AUC: 0.9854 Val AUC: 0.9806 Val PRC: 0.9805 Time: 0.71\n",
      "Epoch: 278 Train Loss: 0.1516 Acc: 0.9272 Pre: 0.9130 Recall: 0.9445 F1: 0.9285 Train AUC: 0.9839 Val AUC: 0.9790 Val PRC: 0.9794 Time: 0.71\n",
      "Epoch: 279 Train Loss: 0.1386 Acc: 0.9225 Pre: 0.9122 Recall: 0.9351 F1: 0.9235 Train AUC: 0.9870 Val AUC: 0.9805 Val PRC: 0.9806 Time: 0.71\n",
      "Epoch: 280 Train Loss: 0.1444 Acc: 0.9236 Pre: 0.9049 Recall: 0.9466 F1: 0.9253 Train AUC: 0.9857 Val AUC: 0.9811 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 281 Train Loss: 0.1407 Acc: 0.9267 Pre: 0.9047 Recall: 0.9539 F1: 0.9286 Train AUC: 0.9865 Val AUC: 0.9804 Val PRC: 0.9811 Time: 0.70\n",
      "Epoch: 282 Train Loss: 0.1470 Acc: 0.9314 Pre: 0.9247 Recall: 0.9393 F1: 0.9319 Train AUC: 0.9852 Val AUC: 0.9804 Val PRC: 0.9808 Time: 0.71\n",
      "Epoch: 283 Train Loss: 0.1356 Acc: 0.9277 Pre: 0.9164 Recall: 0.9414 F1: 0.9287 Train AUC: 0.9876 Val AUC: 0.9792 Val PRC: 0.9797 Time: 0.73\n",
      "Epoch: 284 Train Loss: 0.1385 Acc: 0.9335 Pre: 0.9367 Recall: 0.9298 F1: 0.9333 Train AUC: 0.9869 Val AUC: 0.9814 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 285 Train Loss: 0.1481 Acc: 0.9257 Pre: 0.9127 Recall: 0.9414 F1: 0.9268 Train AUC: 0.9848 Val AUC: 0.9805 Val PRC: 0.9814 Time: 0.71\n",
      "Epoch: 286 Train Loss: 0.1451 Acc: 0.9293 Pre: 0.9150 Recall: 0.9466 F1: 0.9305 Train AUC: 0.9854 Val AUC: 0.9804 Val PRC: 0.9807 Time: 0.72\n",
      "Epoch: 287 Train Loss: 0.1378 Acc: 0.9251 Pre: 0.9328 Recall: 0.9162 F1: 0.9245 Train AUC: 0.9872 Val AUC: 0.9790 Val PRC: 0.9799 Time: 0.72\n",
      "Epoch: 288 Train Loss: 0.1408 Acc: 0.9319 Pre: 0.9231 Recall: 0.9424 F1: 0.9326 Train AUC: 0.9859 Val AUC: 0.9781 Val PRC: 0.9729 Time: 0.70\n",
      "Epoch: 289 Train Loss: 0.1375 Acc: 0.9283 Pre: 0.9191 Recall: 0.9393 F1: 0.9291 Train AUC: 0.9865 Val AUC: 0.9785 Val PRC: 0.9765 Time: 0.72\n",
      "Epoch: 290 Train Loss: 0.1397 Acc: 0.9304 Pre: 0.9135 Recall: 0.9508 F1: 0.9318 Train AUC: 0.9865 Val AUC: 0.9810 Val PRC: 0.9813 Time: 0.71\n",
      "Epoch: 291 Train Loss: 0.1285 Acc: 0.9251 Pre: 0.9151 Recall: 0.9372 F1: 0.9260 Train AUC: 0.9887 Val AUC: 0.9803 Val PRC: 0.9804 Time: 0.73\n",
      "Epoch: 292 Train Loss: 0.1384 Acc: 0.9272 Pre: 0.9008 Recall: 0.9602 F1: 0.9295 Train AUC: 0.9865 Val AUC: 0.9807 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 293 Train Loss: 0.1370 Acc: 0.9283 Pre: 0.9098 Recall: 0.9508 F1: 0.9299 Train AUC: 0.9873 Val AUC: 0.9810 Val PRC: 0.9818 Time: 0.72\n",
      "Epoch: 294 Train Loss: 0.1422 Acc: 0.9277 Pre: 0.9147 Recall: 0.9435 F1: 0.9289 Train AUC: 0.9855 Val AUC: 0.9814 Val PRC: 0.9816 Time: 0.71\n",
      "Epoch: 295 Train Loss: 0.1362 Acc: 0.9262 Pre: 0.9095 Recall: 0.9466 F1: 0.9277 Train AUC: 0.9870 Val AUC: 0.9812 Val PRC: 0.9816 Time: 0.74\n",
      "Epoch: 296 Train Loss: 0.1314 Acc: 0.9288 Pre: 0.9067 Recall: 0.9560 F1: 0.9307 Train AUC: 0.9882 Val AUC: 0.9800 Val PRC: 0.9799 Time: 0.74\n",
      "Epoch: 297 Train Loss: 0.1315 Acc: 0.9272 Pre: 0.9096 Recall: 0.9487 F1: 0.9288 Train AUC: 0.9879 Val AUC: 0.9796 Val PRC: 0.9790 Time: 0.74\n",
      "Epoch: 298 Train Loss: 0.1402 Acc: 0.9288 Pre: 0.9019 Recall: 0.9623 F1: 0.9311 Train AUC: 0.9856 Val AUC: 0.9808 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 299 Train Loss: 0.1468 Acc: 0.9325 Pre: 0.9172 Recall: 0.9508 F1: 0.9337 Train AUC: 0.9848 Val AUC: 0.9808 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 300 Train Loss: 0.1420 Acc: 0.9319 Pre: 0.9266 Recall: 0.9382 F1: 0.9324 Train AUC: 0.9858 Val AUC: 0.9809 Val PRC: 0.9812 Time: 0.72\n",
      "Epoch: 301 Train Loss: 0.1243 Acc: 0.9304 Pre: 0.9102 Recall: 0.9550 F1: 0.9320 Train AUC: 0.9895 Val AUC: 0.9813 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 302 Train Loss: 0.1386 Acc: 0.9257 Pre: 0.8935 Recall: 0.9665 F1: 0.9286 Train AUC: 0.9871 Val AUC: 0.9807 Val PRC: 0.9817 Time: 0.72\n",
      "Epoch: 303 Train Loss: 0.1344 Acc: 0.9241 Pre: 0.9150 Recall: 0.9351 F1: 0.9249 Train AUC: 0.9872 Val AUC: 0.9786 Val PRC: 0.9788 Time: 0.71\n",
      "Epoch: 304 Train Loss: 0.1281 Acc: 0.9283 Pre: 0.9066 Recall: 0.9550 F1: 0.9301 Train AUC: 0.9888 Val AUC: 0.9807 Val PRC: 0.9813 Time: 0.72\n",
      "Epoch: 305 Train Loss: 0.1377 Acc: 0.9293 Pre: 0.9125 Recall: 0.9497 F1: 0.9307 Train AUC: 0.9865 Val AUC: 0.9788 Val PRC: 0.9759 Time: 0.74\n",
      "Epoch: 306 Train Loss: 0.1272 Acc: 0.9304 Pre: 0.9419 Recall: 0.9173 F1: 0.9294 Train AUC: 0.9886 Val AUC: 0.9805 Val PRC: 0.9793 Time: 0.71\n",
      "Epoch: 307 Train Loss: 0.1269 Acc: 0.9340 Pre: 0.9260 Recall: 0.9435 F1: 0.9346 Train AUC: 0.9884 Val AUC: 0.9802 Val PRC: 0.9797 Time: 0.72\n",
      "Epoch: 308 Train Loss: 0.1342 Acc: 0.9309 Pre: 0.9238 Recall: 0.9393 F1: 0.9315 Train AUC: 0.9870 Val AUC: 0.9805 Val PRC: 0.9765 Time: 0.72\n",
      "Epoch: 309 Train Loss: 0.1320 Acc: 0.9288 Pre: 0.8995 Recall: 0.9654 F1: 0.9313 Train AUC: 0.9881 Val AUC: 0.9807 Val PRC: 0.9791 Time: 0.71\n",
      "Epoch: 310 Train Loss: 0.1607 Acc: 0.9309 Pre: 0.9136 Recall: 0.9518 F1: 0.9323 Train AUC: 0.9867 Val AUC: 0.9797 Val PRC: 0.9796 Time: 0.72\n",
      "Epoch: 311 Train Loss: 0.1327 Acc: 0.9330 Pre: 0.9189 Recall: 0.9497 F1: 0.9341 Train AUC: 0.9868 Val AUC: 0.9784 Val PRC: 0.9766 Time: 0.73\n",
      "Epoch: 312 Train Loss: 0.1321 Acc: 0.9262 Pre: 0.9062 Recall: 0.9508 F1: 0.9280 Train AUC: 0.9878 Val AUC: 0.9805 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 313 Train Loss: 0.1347 Acc: 0.9309 Pre: 0.9273 Recall: 0.9351 F1: 0.9312 Train AUC: 0.9873 Val AUC: 0.9811 Val PRC: 0.9821 Time: 0.70\n",
      "Epoch: 314 Train Loss: 0.1258 Acc: 0.9251 Pre: 0.9118 Recall: 0.9414 F1: 0.9263 Train AUC: 0.9888 Val AUC: 0.9805 Val PRC: 0.9815 Time: 0.72\n",
      "Epoch: 315 Train Loss: 0.1407 Acc: 0.9298 Pre: 0.9185 Recall: 0.9435 F1: 0.9308 Train AUC: 0.9855 Val AUC: 0.9807 Val PRC: 0.9818 Time: 0.70\n",
      "Epoch: 316 Train Loss: 0.1284 Acc: 0.9277 Pre: 0.9216 Recall: 0.9351 F1: 0.9283 Train AUC: 0.9886 Val AUC: 0.9808 Val PRC: 0.9814 Time: 0.70\n",
      "Epoch: 317 Train Loss: 0.1336 Acc: 0.9298 Pre: 0.9193 Recall: 0.9424 F1: 0.9307 Train AUC: 0.9869 Val AUC: 0.9780 Val PRC: 0.9781 Time: 0.73\n",
      "Epoch: 318 Train Loss: 0.1286 Acc: 0.9283 Pre: 0.9305 Recall: 0.9257 F1: 0.9281 Train AUC: 0.9878 Val AUC: 0.9804 Val PRC: 0.9767 Time: 0.73\n",
      "Epoch: 319 Train Loss: 0.1254 Acc: 0.9319 Pre: 0.9222 Recall: 0.9435 F1: 0.9327 Train AUC: 0.9886 Val AUC: 0.9810 Val PRC: 0.9764 Time: 0.73\n",
      "Epoch: 320 Train Loss: 0.1327 Acc: 0.9262 Pre: 0.9187 Recall: 0.9351 F1: 0.9268 Train AUC: 0.9874 Val AUC: 0.9790 Val PRC: 0.9801 Time: 0.74\n",
      "Epoch: 321 Train Loss: 0.1346 Acc: 0.9288 Pre: 0.9217 Recall: 0.9372 F1: 0.9294 Train AUC: 0.9864 Val AUC: 0.9793 Val PRC: 0.9761 Time: 0.71\n",
      "Epoch: 322 Train Loss: 0.1259 Acc: 0.9262 Pre: 0.9078 Recall: 0.9487 F1: 0.9278 Train AUC: 0.9881 Val AUC: 0.9791 Val PRC: 0.9793 Time: 0.71\n",
      "Epoch: 323 Train Loss: 0.1235 Acc: 0.9272 Pre: 0.9138 Recall: 0.9435 F1: 0.9284 Train AUC: 0.9891 Val AUC: 0.9812 Val PRC: 0.9820 Time: 0.72\n",
      "Epoch: 324 Train Loss: 0.1292 Acc: 0.9309 Pre: 0.9247 Recall: 0.9382 F1: 0.9314 Train AUC: 0.9876 Val AUC: 0.9801 Val PRC: 0.9802 Time: 0.73\n",
      "Epoch: 325 Train Loss: 0.1223 Acc: 0.9330 Pre: 0.9294 Recall: 0.9372 F1: 0.9333 Train AUC: 0.9889 Val AUC: 0.9815 Val PRC: 0.9824 Time: 0.72\n",
      "Epoch: 326 Train Loss: 0.1337 Acc: 0.9372 Pre: 0.9390 Recall: 0.9351 F1: 0.9370 Train AUC: 0.9877 Val AUC: 0.9814 Val PRC: 0.9820 Time: 0.74\n",
      "Epoch: 327 Train Loss: 0.1293 Acc: 0.9361 Pre: 0.9246 Recall: 0.9497 F1: 0.9370 Train AUC: 0.9878 Val AUC: 0.9818 Val PRC: 0.9824 Time: 0.72\n",
      "Epoch: 328 Train Loss: 0.1360 Acc: 0.9335 Pre: 0.9242 Recall: 0.9445 F1: 0.9342 Train AUC: 0.9867 Val AUC: 0.9807 Val PRC: 0.9807 Time: 0.71\n",
      "Epoch: 329 Train Loss: 0.1233 Acc: 0.9304 Pre: 0.9102 Recall: 0.9550 F1: 0.9320 Train AUC: 0.9887 Val AUC: 0.9817 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 330 Train Loss: 0.1184 Acc: 0.9351 Pre: 0.9388 Recall: 0.9309 F1: 0.9348 Train AUC: 0.9897 Val AUC: 0.9826 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 331 Train Loss: 0.1230 Acc: 0.9340 Pre: 0.9313 Recall: 0.9372 F1: 0.9342 Train AUC: 0.9894 Val AUC: 0.9815 Val PRC: 0.9799 Time: 0.73\n",
      "Epoch: 332 Train Loss: 0.1241 Acc: 0.9298 Pre: 0.9005 Recall: 0.9665 F1: 0.9323 Train AUC: 0.9888 Val AUC: 0.9821 Val PRC: 0.9831 Time: 0.74\n",
      "Epoch: 333 Train Loss: 0.1222 Acc: 0.9366 Pre: 0.9380 Recall: 0.9351 F1: 0.9365 Train AUC: 0.9891 Val AUC: 0.9821 Val PRC: 0.9829 Time: 0.71\n",
      "Epoch: 334 Train Loss: 0.1225 Acc: 0.9230 Pre: 0.8953 Recall: 0.9581 F1: 0.9256 Train AUC: 0.9889 Val AUC: 0.9787 Val PRC: 0.9788 Time: 0.71\n",
      "Epoch: 335 Train Loss: 0.1212 Acc: 0.9314 Pre: 0.9187 Recall: 0.9466 F1: 0.9324 Train AUC: 0.9891 Val AUC: 0.9810 Val PRC: 0.9758 Time: 0.72\n",
      "Epoch: 336 Train Loss: 0.1285 Acc: 0.9330 Pre: 0.9207 Recall: 0.9476 F1: 0.9340 Train AUC: 0.9876 Val AUC: 0.9810 Val PRC: 0.9773 Time: 0.71\n",
      "Epoch: 337 Train Loss: 0.1206 Acc: 0.9325 Pre: 0.9366 Recall: 0.9277 F1: 0.9321 Train AUC: 0.9894 Val AUC: 0.9805 Val PRC: 0.9780 Time: 0.71\n",
      "Epoch: 338 Train Loss: 0.1221 Acc: 0.9319 Pre: 0.9080 Recall: 0.9613 F1: 0.9339 Train AUC: 0.9885 Val AUC: 0.9813 Val PRC: 0.9809 Time: 0.72\n",
      "Epoch: 339 Train Loss: 0.1213 Acc: 0.9277 Pre: 0.9106 Recall: 0.9487 F1: 0.9292 Train AUC: 0.9893 Val AUC: 0.9792 Val PRC: 0.9792 Time: 0.72\n",
      "Epoch: 340 Train Loss: 0.1203 Acc: 0.9293 Pre: 0.9289 Recall: 0.9298 F1: 0.9294 Train AUC: 0.9893 Val AUC: 0.9812 Val PRC: 0.9818 Time: 0.70\n",
      "Epoch: 341 Train Loss: 0.1222 Acc: 0.9298 Pre: 0.9202 Recall: 0.9414 F1: 0.9306 Train AUC: 0.9890 Val AUC: 0.9788 Val PRC: 0.9785 Time: 0.71\n",
      "Epoch: 342 Train Loss: 0.1261 Acc: 0.9309 Pre: 0.9161 Recall: 0.9487 F1: 0.9321 Train AUC: 0.9883 Val AUC: 0.9801 Val PRC: 0.9804 Time: 0.71\n",
      "Epoch: 343 Train Loss: 0.1273 Acc: 0.9293 Pre: 0.9028 Recall: 0.9623 F1: 0.9316 Train AUC: 0.9881 Val AUC: 0.9818 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 344 Train Loss: 0.1219 Acc: 0.9325 Pre: 0.9163 Recall: 0.9518 F1: 0.9337 Train AUC: 0.9889 Val AUC: 0.9813 Val PRC: 0.9820 Time: 0.71\n",
      "Epoch: 345 Train Loss: 0.1150 Acc: 0.9351 Pre: 0.9210 Recall: 0.9518 F1: 0.9361 Train AUC: 0.9900 Val AUC: 0.9819 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 346 Train Loss: 0.1236 Acc: 0.9272 Pre: 0.9340 Recall: 0.9194 F1: 0.9266 Train AUC: 0.9893 Val AUC: 0.9789 Val PRC: 0.9789 Time: 0.72\n",
      "Epoch: 347 Train Loss: 0.1177 Acc: 0.9366 Pre: 0.9326 Recall: 0.9414 F1: 0.9369 Train AUC: 0.9899 Val AUC: 0.9805 Val PRC: 0.9795 Time: 0.73\n",
      "Epoch: 348 Train Loss: 0.1171 Acc: 0.9319 Pre: 0.9113 Recall: 0.9571 F1: 0.9336 Train AUC: 0.9897 Val AUC: 0.9800 Val PRC: 0.9801 Time: 0.74\n",
      "Epoch: 349 Train Loss: 0.1204 Acc: 0.9314 Pre: 0.9145 Recall: 0.9518 F1: 0.9328 Train AUC: 0.9894 Val AUC: 0.9799 Val PRC: 0.9796 Time: 0.71\n",
      "Epoch: 350 Train Loss: 0.1255 Acc: 0.9319 Pre: 0.9154 Recall: 0.9518 F1: 0.9333 Train AUC: 0.9889 Val AUC: 0.9806 Val PRC: 0.9817 Time: 0.72\n",
      "Epoch: 351 Train Loss: 0.1189 Acc: 0.9351 Pre: 0.9279 Recall: 0.9435 F1: 0.9356 Train AUC: 0.9898 Val AUC: 0.9824 Val PRC: 0.9835 Time: 0.74\n",
      "Epoch: 352 Train Loss: 0.1157 Acc: 0.9361 Pre: 0.9246 Recall: 0.9497 F1: 0.9370 Train AUC: 0.9900 Val AUC: 0.9807 Val PRC: 0.9812 Time: 0.71\n",
      "Epoch: 353 Train Loss: 0.1242 Acc: 0.9298 Pre: 0.9185 Recall: 0.9435 F1: 0.9308 Train AUC: 0.9882 Val AUC: 0.9803 Val PRC: 0.9800 Time: 0.72\n",
      "Epoch: 354 Train Loss: 0.1315 Acc: 0.9319 Pre: 0.9104 Recall: 0.9581 F1: 0.9337 Train AUC: 0.9865 Val AUC: 0.9816 Val PRC: 0.9827 Time: 0.73\n",
      "Epoch: 355 Train Loss: 0.1131 Acc: 0.9325 Pre: 0.9172 Recall: 0.9508 F1: 0.9337 Train AUC: 0.9907 Val AUC: 0.9822 Val PRC: 0.9828 Time: 0.74\n",
      "Epoch: 356 Train Loss: 0.1195 Acc: 0.9356 Pre: 0.9254 Recall: 0.9476 F1: 0.9364 Train AUC: 0.9895 Val AUC: 0.9811 Val PRC: 0.9821 Time: 0.73\n",
      "Epoch: 357 Train Loss: 0.1144 Acc: 0.9408 Pre: 0.9395 Recall: 0.9424 F1: 0.9409 Train AUC: 0.9897 Val AUC: 0.9825 Val PRC: 0.9829 Time: 0.72\n",
      "Epoch: 358 Train Loss: 0.1173 Acc: 0.9325 Pre: 0.9155 Recall: 0.9529 F1: 0.9338 Train AUC: 0.9893 Val AUC: 0.9810 Val PRC: 0.9812 Time: 0.72\n",
      "Epoch: 359 Train Loss: 0.1251 Acc: 0.9262 Pre: 0.9006 Recall: 0.9581 F1: 0.9285 Train AUC: 0.9877 Val AUC: 0.9798 Val PRC: 0.9800 Time: 0.73\n",
      "Epoch: 360 Train Loss: 0.1193 Acc: 0.9351 Pre: 0.9397 Recall: 0.9298 F1: 0.9347 Train AUC: 0.9892 Val AUC: 0.9821 Val PRC: 0.9818 Time: 0.74\n",
      "Epoch: 361 Train Loss: 0.1223 Acc: 0.9288 Pre: 0.9252 Recall: 0.9330 F1: 0.9291 Train AUC: 0.9890 Val AUC: 0.9814 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 362 Train Loss: 0.1171 Acc: 0.9267 Pre: 0.9031 Recall: 0.9560 F1: 0.9288 Train AUC: 0.9896 Val AUC: 0.9806 Val PRC: 0.9813 Time: 0.71\n",
      "Epoch: 363 Train Loss: 0.1190 Acc: 0.9330 Pre: 0.9198 Recall: 0.9487 F1: 0.9340 Train AUC: 0.9898 Val AUC: 0.9835 Val PRC: 0.9850 Time: 0.72\n",
      "Epoch: 364 Train Loss: 0.1186 Acc: 0.9366 Pre: 0.9380 Recall: 0.9351 F1: 0.9365 Train AUC: 0.9899 Val AUC: 0.9829 Val PRC: 0.9840 Time: 0.71\n",
      "Epoch: 365 Train Loss: 0.1151 Acc: 0.9325 Pre: 0.9347 Recall: 0.9298 F1: 0.9323 Train AUC: 0.9903 Val AUC: 0.9807 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 366 Train Loss: 0.1163 Acc: 0.9325 Pre: 0.9223 Recall: 0.9445 F1: 0.9333 Train AUC: 0.9897 Val AUC: 0.9820 Val PRC: 0.9826 Time: 0.76\n",
      "Epoch: 367 Train Loss: 0.1172 Acc: 0.9356 Pre: 0.9211 Recall: 0.9529 F1: 0.9367 Train AUC: 0.9894 Val AUC: 0.9812 Val PRC: 0.9814 Time: 0.72\n",
      "Epoch: 368 Train Loss: 0.1131 Acc: 0.9293 Pre: 0.9043 Recall: 0.9602 F1: 0.9314 Train AUC: 0.9909 Val AUC: 0.9807 Val PRC: 0.9803 Time: 0.70\n",
      "Epoch: 369 Train Loss: 0.1141 Acc: 0.9325 Pre: 0.9422 Recall: 0.9215 F1: 0.9317 Train AUC: 0.9898 Val AUC: 0.9811 Val PRC: 0.9809 Time: 0.74\n",
      "Epoch: 370 Train Loss: 0.1167 Acc: 0.9325 Pre: 0.9138 Recall: 0.9550 F1: 0.9339 Train AUC: 0.9893 Val AUC: 0.9807 Val PRC: 0.9805 Time: 0.72\n",
      "Epoch: 371 Train Loss: 0.1246 Acc: 0.9330 Pre: 0.9276 Recall: 0.9393 F1: 0.9334 Train AUC: 0.9886 Val AUC: 0.9807 Val PRC: 0.9817 Time: 0.71\n",
      "Epoch: 372 Train Loss: 0.1131 Acc: 0.9366 Pre: 0.9417 Recall: 0.9309 F1: 0.9363 Train AUC: 0.9904 Val AUC: 0.9807 Val PRC: 0.9791 Time: 0.72\n",
      "Epoch: 373 Train Loss: 0.1094 Acc: 0.9356 Pre: 0.9426 Recall: 0.9277 F1: 0.9351 Train AUC: 0.9906 Val AUC: 0.9810 Val PRC: 0.9811 Time: 0.73\n",
      "Epoch: 374 Train Loss: 0.1167 Acc: 0.9319 Pre: 0.9222 Recall: 0.9435 F1: 0.9327 Train AUC: 0.9894 Val AUC: 0.9809 Val PRC: 0.9810 Time: 0.73\n",
      "Epoch: 375 Train Loss: 0.1149 Acc: 0.9335 Pre: 0.9295 Recall: 0.9382 F1: 0.9338 Train AUC: 0.9896 Val AUC: 0.9821 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 376 Train Loss: 0.1094 Acc: 0.9382 Pre: 0.9391 Recall: 0.9372 F1: 0.9382 Train AUC: 0.9911 Val AUC: 0.9825 Val PRC: 0.9835 Time: 0.72\n",
      "Epoch: 377 Train Loss: 0.1127 Acc: 0.9366 Pre: 0.9326 Recall: 0.9414 F1: 0.9369 Train AUC: 0.9904 Val AUC: 0.9806 Val PRC: 0.9805 Time: 0.73\n",
      "Epoch: 378 Train Loss: 0.1191 Acc: 0.9382 Pre: 0.9275 Recall: 0.9508 F1: 0.9390 Train AUC: 0.9892 Val AUC: 0.9816 Val PRC: 0.9821 Time: 0.74\n",
      "Epoch: 379 Train Loss: 0.1194 Acc: 0.9346 Pre: 0.9350 Recall: 0.9340 F1: 0.9345 Train AUC: 0.9893 Val AUC: 0.9828 Val PRC: 0.9838 Time: 0.73\n",
      "Epoch: 380 Train Loss: 0.1195 Acc: 0.9293 Pre: 0.9125 Recall: 0.9497 F1: 0.9307 Train AUC: 0.9899 Val AUC: 0.9813 Val PRC: 0.9823 Time: 0.93\n",
      "Epoch: 381 Train Loss: 0.1123 Acc: 0.9372 Pre: 0.9418 Recall: 0.9319 F1: 0.9368 Train AUC: 0.9901 Val AUC: 0.9829 Val PRC: 0.9841 Time: 0.74\n",
      "Epoch: 382 Train Loss: 0.1102 Acc: 0.9387 Pre: 0.9392 Recall: 0.9382 F1: 0.9387 Train AUC: 0.9906 Val AUC: 0.9820 Val PRC: 0.9832 Time: 0.72\n",
      "Epoch: 383 Train Loss: 0.1132 Acc: 0.9361 Pre: 0.9417 Recall: 0.9298 F1: 0.9357 Train AUC: 0.9900 Val AUC: 0.9813 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 384 Train Loss: 0.1144 Acc: 0.9366 Pre: 0.9389 Recall: 0.9340 F1: 0.9365 Train AUC: 0.9897 Val AUC: 0.9810 Val PRC: 0.9768 Time: 0.73\n",
      "Epoch: 385 Train Loss: 0.1064 Acc: 0.9366 Pre: 0.9399 Recall: 0.9330 F1: 0.9364 Train AUC: 0.9914 Val AUC: 0.9814 Val PRC: 0.9825 Time: 0.73\n",
      "Epoch: 386 Train Loss: 0.1062 Acc: 0.9309 Pre: 0.9144 Recall: 0.9508 F1: 0.9322 Train AUC: 0.9907 Val AUC: 0.9807 Val PRC: 0.9803 Time: 0.72\n",
      "Epoch: 387 Train Loss: 0.1109 Acc: 0.9356 Pre: 0.9211 Recall: 0.9529 F1: 0.9367 Train AUC: 0.9898 Val AUC: 0.9824 Val PRC: 0.9830 Time: 0.74\n",
      "Epoch: 388 Train Loss: 0.1074 Acc: 0.9335 Pre: 0.9295 Recall: 0.9382 F1: 0.9338 Train AUC: 0.9909 Val AUC: 0.9820 Val PRC: 0.9833 Time: 0.73\n",
      "Epoch: 389 Train Loss: 0.1075 Acc: 0.9366 Pre: 0.9308 Recall: 0.9435 F1: 0.9371 Train AUC: 0.9913 Val AUC: 0.9814 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 390 Train Loss: 0.1126 Acc: 0.9304 Pre: 0.9160 Recall: 0.9476 F1: 0.9315 Train AUC: 0.9898 Val AUC: 0.9812 Val PRC: 0.9822 Time: 0.72\n",
      "Epoch: 391 Train Loss: 0.1104 Acc: 0.9356 Pre: 0.9280 Recall: 0.9445 F1: 0.9362 Train AUC: 0.9907 Val AUC: 0.9821 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 392 Train Loss: 0.1131 Acc: 0.9340 Pre: 0.9368 Recall: 0.9309 F1: 0.9338 Train AUC: 0.9900 Val AUC: 0.9823 Val PRC: 0.9827 Time: 0.71\n",
      "Epoch: 393 Train Loss: 0.1039 Acc: 0.9330 Pre: 0.9215 Recall: 0.9466 F1: 0.9339 Train AUC: 0.9915 Val AUC: 0.9828 Val PRC: 0.9835 Time: 0.71\n",
      "Epoch: 394 Train Loss: 0.1159 Acc: 0.9346 Pre: 0.9287 Recall: 0.9414 F1: 0.9350 Train AUC: 0.9890 Val AUC: 0.9814 Val PRC: 0.9813 Time: 0.73\n",
      "Epoch: 395 Train Loss: 0.1097 Acc: 0.9382 Pre: 0.9319 Recall: 0.9455 F1: 0.9387 Train AUC: 0.9901 Val AUC: 0.9814 Val PRC: 0.9811 Time: 0.70\n",
      "Epoch: 396 Train Loss: 0.1163 Acc: 0.9346 Pre: 0.9167 Recall: 0.9560 F1: 0.9359 Train AUC: 0.9887 Val AUC: 0.9802 Val PRC: 0.9799 Time: 0.72\n",
      "Epoch: 397 Train Loss: 0.1015 Acc: 0.9387 Pre: 0.9284 Recall: 0.9508 F1: 0.9395 Train AUC: 0.9920 Val AUC: 0.9829 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 398 Train Loss: 0.1107 Acc: 0.9377 Pre: 0.9345 Recall: 0.9414 F1: 0.9379 Train AUC: 0.9900 Val AUC: 0.9821 Val PRC: 0.9828 Time: 0.70\n",
      "Epoch: 399 Train Loss: 0.1102 Acc: 0.9377 Pre: 0.9363 Recall: 0.9393 F1: 0.9378 Train AUC: 0.9897 Val AUC: 0.9820 Val PRC: 0.9820 Time: 0.71\n",
      "Epoch: 400 Train Loss: 0.0989 Acc: 0.9319 Pre: 0.9248 Recall: 0.9403 F1: 0.9325 Train AUC: 0.9921 Val AUC: 0.9812 Val PRC: 0.9808 Time: 0.70\n",
      "Epoch: 401 Train Loss: 0.1087 Acc: 0.9283 Pre: 0.9074 Recall: 0.9539 F1: 0.9301 Train AUC: 0.9908 Val AUC: 0.9797 Val PRC: 0.9807 Time: 0.72\n",
      "Epoch: 402 Train Loss: 0.1059 Acc: 0.9325 Pre: 0.9122 Recall: 0.9571 F1: 0.9341 Train AUC: 0.9912 Val AUC: 0.9818 Val PRC: 0.9830 Time: 0.71\n",
      "Epoch: 403 Train Loss: 0.1030 Acc: 0.9393 Pre: 0.9420 Recall: 0.9361 F1: 0.9391 Train AUC: 0.9914 Val AUC: 0.9812 Val PRC: 0.9823 Time: 0.72\n",
      "Epoch: 404 Train Loss: 0.1077 Acc: 0.9398 Pre: 0.9330 Recall: 0.9476 F1: 0.9403 Train AUC: 0.9908 Val AUC: 0.9833 Val PRC: 0.9841 Time: 0.72\n",
      "Epoch: 405 Train Loss: 0.1037 Acc: 0.9309 Pre: 0.9136 Recall: 0.9518 F1: 0.9323 Train AUC: 0.9911 Val AUC: 0.9816 Val PRC: 0.9829 Time: 0.72\n",
      "Epoch: 406 Train Loss: 0.1003 Acc: 0.9351 Pre: 0.9306 Recall: 0.9403 F1: 0.9354 Train AUC: 0.9919 Val AUC: 0.9821 Val PRC: 0.9830 Time: 0.72\n",
      "Epoch: 407 Train Loss: 0.1103 Acc: 0.9346 Pre: 0.9368 Recall: 0.9319 F1: 0.9344 Train AUC: 0.9901 Val AUC: 0.9839 Val PRC: 0.9851 Time: 0.71\n",
      "Epoch: 408 Train Loss: 0.1074 Acc: 0.9351 Pre: 0.9360 Recall: 0.9340 F1: 0.9350 Train AUC: 0.9910 Val AUC: 0.9817 Val PRC: 0.9828 Time: 0.76\n",
      "Epoch: 409 Train Loss: 0.0986 Acc: 0.9366 Pre: 0.9326 Recall: 0.9414 F1: 0.9369 Train AUC: 0.9929 Val AUC: 0.9826 Val PRC: 0.9831 Time: 0.73\n",
      "Epoch: 410 Train Loss: 0.1099 Acc: 0.9356 Pre: 0.9315 Recall: 0.9403 F1: 0.9359 Train AUC: 0.9901 Val AUC: 0.9787 Val PRC: 0.9761 Time: 0.71\n",
      "Epoch: 411 Train Loss: 0.0939 Acc: 0.9372 Pre: 0.9372 Recall: 0.9372 F1: 0.9372 Train AUC: 0.9933 Val AUC: 0.9820 Val PRC: 0.9822 Time: 0.71\n",
      "Epoch: 412 Train Loss: 0.1261 Acc: 0.9351 Pre: 0.9227 Recall: 0.9497 F1: 0.9360 Train AUC: 0.9899 Val AUC: 0.9814 Val PRC: 0.9826 Time: 0.72\n",
      "Epoch: 413 Train Loss: 0.1478 Acc: 0.9340 Pre: 0.9183 Recall: 0.9529 F1: 0.9353 Train AUC: 0.9886 Val AUC: 0.9815 Val PRC: 0.9827 Time: 0.71\n",
      "Epoch: 414 Train Loss: 0.1054 Acc: 0.9351 Pre: 0.9360 Recall: 0.9340 F1: 0.9350 Train AUC: 0.9913 Val AUC: 0.9825 Val PRC: 0.9833 Time: 0.74\n",
      "Epoch: 415 Train Loss: 0.1306 Acc: 0.9309 Pre: 0.9411 Recall: 0.9194 F1: 0.9301 Train AUC: 0.9904 Val AUC: 0.9797 Val PRC: 0.9801 Time: 0.73\n",
      "Epoch: 416 Train Loss: 0.1222 Acc: 0.9335 Pre: 0.9207 Recall: 0.9487 F1: 0.9345 Train AUC: 0.9904 Val AUC: 0.9840 Val PRC: 0.9853 Time: 0.73\n",
      "Epoch: 417 Train Loss: 0.1053 Acc: 0.9382 Pre: 0.9319 Recall: 0.9455 F1: 0.9387 Train AUC: 0.9909 Val AUC: 0.9825 Val PRC: 0.9834 Time: 0.71\n",
      "Epoch: 418 Train Loss: 0.1106 Acc: 0.9366 Pre: 0.9326 Recall: 0.9414 F1: 0.9369 Train AUC: 0.9903 Val AUC: 0.9838 Val PRC: 0.9852 Time: 0.73\n",
      "Epoch: 419 Train Loss: 0.1091 Acc: 0.9366 Pre: 0.9353 Recall: 0.9382 F1: 0.9367 Train AUC: 0.9908 Val AUC: 0.9833 Val PRC: 0.9848 Time: 0.71\n",
      "Epoch: 420 Train Loss: 0.1053 Acc: 0.9309 Pre: 0.9291 Recall: 0.9330 F1: 0.9310 Train AUC: 0.9914 Val AUC: 0.9814 Val PRC: 0.9825 Time: 0.72\n",
      "Epoch: 421 Train Loss: 0.0989 Acc: 0.9382 Pre: 0.9391 Recall: 0.9372 F1: 0.9382 Train AUC: 0.9917 Val AUC: 0.9819 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 422 Train Loss: 0.1101 Acc: 0.9398 Pre: 0.9516 Recall: 0.9267 F1: 0.9390 Train AUC: 0.9906 Val AUC: 0.9840 Val PRC: 0.9849 Time: 0.72\n",
      "Epoch: 423 Train Loss: 0.1133 Acc: 0.9429 Pre: 0.9406 Recall: 0.9455 F1: 0.9431 Train AUC: 0.9905 Val AUC: 0.9837 Val PRC: 0.9845 Time: 0.71\n",
      "Epoch: 424 Train Loss: 0.1054 Acc: 0.9335 Pre: 0.9199 Recall: 0.9497 F1: 0.9346 Train AUC: 0.9913 Val AUC: 0.9831 Val PRC: 0.9839 Time: 0.73\n",
      "Epoch: 425 Train Loss: 0.1023 Acc: 0.9361 Pre: 0.9263 Recall: 0.9476 F1: 0.9369 Train AUC: 0.9919 Val AUC: 0.9826 Val PRC: 0.9835 Time: 0.72\n",
      "Epoch: 426 Train Loss: 0.1069 Acc: 0.9398 Pre: 0.9506 Recall: 0.9277 F1: 0.9391 Train AUC: 0.9910 Val AUC: 0.9840 Val PRC: 0.9850 Time: 0.71\n",
      "Epoch: 427 Train Loss: 0.1064 Acc: 0.9356 Pre: 0.9454 Recall: 0.9246 F1: 0.9349 Train AUC: 0.9914 Val AUC: 0.9812 Val PRC: 0.9819 Time: 0.72\n",
      "Epoch: 428 Train Loss: 0.0975 Acc: 0.9414 Pre: 0.9518 Recall: 0.9298 F1: 0.9407 Train AUC: 0.9927 Val AUC: 0.9843 Val PRC: 0.9854 Time: 0.72\n",
      "Epoch: 429 Train Loss: 0.1027 Acc: 0.9414 Pre: 0.9527 Recall: 0.9288 F1: 0.9406 Train AUC: 0.9920 Val AUC: 0.9836 Val PRC: 0.9847 Time: 0.73\n",
      "Epoch: 430 Train Loss: 0.1012 Acc: 0.9361 Pre: 0.9417 Recall: 0.9298 F1: 0.9357 Train AUC: 0.9914 Val AUC: 0.9826 Val PRC: 0.9831 Time: 0.72\n",
      "Epoch: 431 Train Loss: 0.1040 Acc: 0.9382 Pre: 0.9476 Recall: 0.9277 F1: 0.9376 Train AUC: 0.9912 Val AUC: 0.9826 Val PRC: 0.9833 Time: 0.71\n",
      "Epoch: 432 Train Loss: 0.1042 Acc: 0.9335 Pre: 0.9199 Recall: 0.9497 F1: 0.9346 Train AUC: 0.9914 Val AUC: 0.9823 Val PRC: 0.9828 Time: 0.71\n",
      "Epoch: 433 Train Loss: 0.1088 Acc: 0.9372 Pre: 0.9335 Recall: 0.9414 F1: 0.9374 Train AUC: 0.9896 Val AUC: 0.9829 Val PRC: 0.9830 Time: 0.73\n",
      "Epoch: 434 Train Loss: 0.1013 Acc: 0.9372 Pre: 0.9475 Recall: 0.9257 F1: 0.9364 Train AUC: 0.9914 Val AUC: 0.9832 Val PRC: 0.9844 Time: 0.71\n",
      "Epoch: 435 Train Loss: 0.1017 Acc: 0.9335 Pre: 0.9404 Recall: 0.9257 F1: 0.9330 Train AUC: 0.9918 Val AUC: 0.9809 Val PRC: 0.9808 Time: 0.70\n",
      "Epoch: 436 Train Loss: 0.0951 Acc: 0.9366 Pre: 0.9238 Recall: 0.9518 F1: 0.9376 Train AUC: 0.9925 Val AUC: 0.9827 Val PRC: 0.9831 Time: 0.73\n",
      "Epoch: 437 Train Loss: 0.1098 Acc: 0.9340 Pre: 0.9208 Recall: 0.9497 F1: 0.9351 Train AUC: 0.9898 Val AUC: 0.9822 Val PRC: 0.9829 Time: 0.72\n",
      "Epoch: 438 Train Loss: 0.1042 Acc: 0.9361 Pre: 0.9272 Recall: 0.9466 F1: 0.9368 Train AUC: 0.9912 Val AUC: 0.9824 Val PRC: 0.9833 Time: 0.72\n",
      "Epoch: 439 Train Loss: 0.1065 Acc: 0.9351 Pre: 0.9297 Recall: 0.9414 F1: 0.9355 Train AUC: 0.9903 Val AUC: 0.9820 Val PRC: 0.9825 Time: 0.71\n",
      "Epoch: 440 Train Loss: 0.0979 Acc: 0.9372 Pre: 0.9221 Recall: 0.9550 F1: 0.9383 Train AUC: 0.9920 Val AUC: 0.9837 Val PRC: 0.9848 Time: 0.72\n",
      "Epoch: 441 Train Loss: 0.0980 Acc: 0.9429 Pre: 0.9490 Recall: 0.9361 F1: 0.9425 Train AUC: 0.9919 Val AUC: 0.9844 Val PRC: 0.9850 Time: 0.71\n",
      "Epoch: 442 Train Loss: 0.1025 Acc: 0.9372 Pre: 0.9372 Recall: 0.9372 F1: 0.9372 Train AUC: 0.9916 Val AUC: 0.9823 Val PRC: 0.9822 Time: 0.72\n",
      "Epoch: 443 Train Loss: 0.0944 Acc: 0.9387 Pre: 0.9486 Recall: 0.9277 F1: 0.9381 Train AUC: 0.9926 Val AUC: 0.9834 Val PRC: 0.9845 Time: 0.74\n",
      "Epoch: 444 Train Loss: 0.1144 Acc: 0.9361 Pre: 0.9445 Recall: 0.9267 F1: 0.9355 Train AUC: 0.9912 Val AUC: 0.9837 Val PRC: 0.9847 Time: 0.73\n",
      "Epoch: 445 Train Loss: 0.0933 Acc: 0.9393 Pre: 0.9250 Recall: 0.9560 F1: 0.9403 Train AUC: 0.9933 Val AUC: 0.9837 Val PRC: 0.9850 Time: 0.71\n",
      "Epoch: 446 Train Loss: 0.0981 Acc: 0.9398 Pre: 0.9357 Recall: 0.9445 F1: 0.9401 Train AUC: 0.9920 Val AUC: 0.9841 Val PRC: 0.9857 Time: 0.73\n",
      "Epoch: 447 Train Loss: 0.0991 Acc: 0.9377 Pre: 0.9514 Recall: 0.9225 F1: 0.9367 Train AUC: 0.9917 Val AUC: 0.9827 Val PRC: 0.9821 Time: 0.72\n",
      "Epoch: 448 Train Loss: 0.0926 Acc: 0.9403 Pre: 0.9507 Recall: 0.9288 F1: 0.9396 Train AUC: 0.9924 Val AUC: 0.9846 Val PRC: 0.9857 Time: 0.73\n",
      "Epoch: 449 Train Loss: 0.1054 Acc: 0.9403 Pre: 0.9546 Recall: 0.9246 F1: 0.9394 Train AUC: 0.9904 Val AUC: 0.9830 Val PRC: 0.9833 Time: 0.72\n",
      "Epoch: 450 Train Loss: 0.0942 Acc: 0.9403 Pre: 0.9526 Recall: 0.9267 F1: 0.9395 Train AUC: 0.9924 Val AUC: 0.9816 Val PRC: 0.9804 Time: 0.73\n",
      "Epoch: 451 Train Loss: 0.0907 Acc: 0.9419 Pre: 0.9499 Recall: 0.9330 F1: 0.9414 Train AUC: 0.9928 Val AUC: 0.9832 Val PRC: 0.9845 Time: 0.71\n",
      "Epoch: 452 Train Loss: 0.0933 Acc: 0.9346 Pre: 0.9192 Recall: 0.9529 F1: 0.9357 Train AUC: 0.9924 Val AUC: 0.9838 Val PRC: 0.9844 Time: 0.72\n",
      "Epoch: 453 Train Loss: 0.0946 Acc: 0.9393 Pre: 0.9605 Recall: 0.9162 F1: 0.9378 Train AUC: 0.9926 Val AUC: 0.9836 Val PRC: 0.9853 Time: 0.72\n",
      "Epoch: 454 Train Loss: 0.0922 Acc: 0.9387 Pre: 0.9401 Recall: 0.9372 F1: 0.9386 Train AUC: 0.9929 Val AUC: 0.9836 Val PRC: 0.9846 Time: 0.72\n",
      "Epoch: 455 Train Loss: 0.0924 Acc: 0.9414 Pre: 0.9489 Recall: 0.9330 F1: 0.9409 Train AUC: 0.9927 Val AUC: 0.9840 Val PRC: 0.9853 Time: 0.72\n",
      "Epoch: 456 Train Loss: 0.0905 Acc: 0.9366 Pre: 0.9408 Recall: 0.9319 F1: 0.9363 Train AUC: 0.9922 Val AUC: 0.9825 Val PRC: 0.9837 Time: 0.72\n",
      "Epoch: 457 Train Loss: 0.1021 Acc: 0.9340 Pre: 0.9304 Recall: 0.9382 F1: 0.9343 Train AUC: 0.9909 Val AUC: 0.9837 Val PRC: 0.9849 Time: 0.72\n",
      "Epoch: 458 Train Loss: 0.0975 Acc: 0.9387 Pre: 0.9284 Recall: 0.9508 F1: 0.9395 Train AUC: 0.9919 Val AUC: 0.9835 Val PRC: 0.9848 Time: 0.74\n",
      "Epoch: 459 Train Loss: 0.0923 Acc: 0.9351 Pre: 0.9351 Recall: 0.9351 F1: 0.9351 Train AUC: 0.9926 Val AUC: 0.9824 Val PRC: 0.9827 Time: 0.71\n",
      "Epoch: 460 Train Loss: 0.0872 Acc: 0.9429 Pre: 0.9415 Recall: 0.9445 F1: 0.9430 Train AUC: 0.9934 Val AUC: 0.9843 Val PRC: 0.9858 Time: 0.71\n",
      "Epoch: 461 Train Loss: 0.1004 Acc: 0.9387 Pre: 0.9411 Recall: 0.9361 F1: 0.9386 Train AUC: 0.9911 Val AUC: 0.9837 Val PRC: 0.9848 Time: 0.72\n",
      "Epoch: 462 Train Loss: 0.0874 Acc: 0.9356 Pre: 0.9379 Recall: 0.9330 F1: 0.9354 Train AUC: 0.9937 Val AUC: 0.9831 Val PRC: 0.9843 Time: 0.73\n",
      "Epoch: 463 Train Loss: 0.0886 Acc: 0.9419 Pre: 0.9597 Recall: 0.9225 F1: 0.9407 Train AUC: 0.9931 Val AUC: 0.9835 Val PRC: 0.9836 Time: 0.73\n",
      "Epoch: 464 Train Loss: 0.0997 Acc: 0.9382 Pre: 0.9419 Recall: 0.9340 F1: 0.9380 Train AUC: 0.9917 Val AUC: 0.9821 Val PRC: 0.9820 Time: 0.74\n",
      "Epoch: 465 Train Loss: 0.0941 Acc: 0.9366 Pre: 0.9399 Recall: 0.9330 F1: 0.9364 Train AUC: 0.9925 Val AUC: 0.9816 Val PRC: 0.9827 Time: 0.72\n",
      "Epoch: 466 Train Loss: 0.0872 Acc: 0.9293 Pre: 0.9067 Recall: 0.9571 F1: 0.9312 Train AUC: 0.9936 Val AUC: 0.9812 Val PRC: 0.9820 Time: 0.73\n",
      "Epoch: 467 Train Loss: 0.0957 Acc: 0.9398 Pre: 0.9555 Recall: 0.9225 F1: 0.9387 Train AUC: 0.9923 Val AUC: 0.9816 Val PRC: 0.9830 Time: 0.73\n",
      "Epoch: 468 Train Loss: 0.0876 Acc: 0.9372 Pre: 0.9265 Recall: 0.9497 F1: 0.9380 Train AUC: 0.9927 Val AUC: 0.9834 Val PRC: 0.9846 Time: 0.71\n",
      "Epoch: 469 Train Loss: 0.0941 Acc: 0.9387 Pre: 0.9356 Recall: 0.9424 F1: 0.9390 Train AUC: 0.9925 Val AUC: 0.9817 Val PRC: 0.9823 Time: 0.72\n",
      "Epoch: 470 Train Loss: 0.0847 Acc: 0.9356 Pre: 0.9483 Recall: 0.9215 F1: 0.9347 Train AUC: 0.9934 Val AUC: 0.9812 Val PRC: 0.9816 Time: 0.73\n",
      "Epoch: 471 Train Loss: 0.0841 Acc: 0.9403 Pre: 0.9450 Recall: 0.9351 F1: 0.9400 Train AUC: 0.9940 Val AUC: 0.9816 Val PRC: 0.9821 Time: 0.73\n",
      "Epoch: 472 Train Loss: 0.0836 Acc: 0.9372 Pre: 0.9282 Recall: 0.9476 F1: 0.9378 Train AUC: 0.9934 Val AUC: 0.9822 Val PRC: 0.9832 Time: 0.72\n",
      "Epoch: 473 Train Loss: 0.0935 Acc: 0.9361 Pre: 0.9455 Recall: 0.9257 F1: 0.9354 Train AUC: 0.9925 Val AUC: 0.9837 Val PRC: 0.9850 Time: 0.76\n",
      "Epoch: 474 Train Loss: 0.0928 Acc: 0.9346 Pre: 0.9378 Recall: 0.9309 F1: 0.9343 Train AUC: 0.9923 Val AUC: 0.9825 Val PRC: 0.9839 Time: 0.74\n",
      "Epoch: 475 Train Loss: 0.0932 Acc: 0.9372 Pre: 0.9446 Recall: 0.9288 F1: 0.9366 Train AUC: 0.9921 Val AUC: 0.9829 Val PRC: 0.9842 Time: 0.74\n",
      "Epoch: 476 Train Loss: 0.0915 Acc: 0.9419 Pre: 0.9480 Recall: 0.9351 F1: 0.9415 Train AUC: 0.9921 Val AUC: 0.9835 Val PRC: 0.9848 Time: 0.76\n",
      "Epoch: 477 Train Loss: 0.0911 Acc: 0.9330 Pre: 0.9114 Recall: 0.9592 F1: 0.9347 Train AUC: 0.9924 Val AUC: 0.9828 Val PRC: 0.9837 Time: 0.74\n",
      "Epoch: 478 Train Loss: 0.1070 Acc: 0.9408 Pre: 0.9422 Recall: 0.9393 F1: 0.9407 Train AUC: 0.9919 Val AUC: 0.9827 Val PRC: 0.9839 Time: 0.70\n",
      "Epoch: 479 Train Loss: 0.0935 Acc: 0.9372 Pre: 0.9399 Recall: 0.9340 F1: 0.9370 Train AUC: 0.9918 Val AUC: 0.9834 Val PRC: 0.9836 Time: 0.72\n",
      "Epoch: 480 Train Loss: 0.0845 Acc: 0.9377 Pre: 0.9428 Recall: 0.9319 F1: 0.9373 Train AUC: 0.9937 Val AUC: 0.9833 Val PRC: 0.9841 Time: 0.70\n",
      "Epoch: 481 Train Loss: 0.0897 Acc: 0.9435 Pre: 0.9407 Recall: 0.9466 F1: 0.9436 Train AUC: 0.9927 Val AUC: 0.9840 Val PRC: 0.9849 Time: 0.70\n",
      "Epoch: 482 Train Loss: 0.0878 Acc: 0.9461 Pre: 0.9561 Recall: 0.9351 F1: 0.9455 Train AUC: 0.9930 Val AUC: 0.9846 Val PRC: 0.9856 Time: 0.71\n",
      "Epoch: 483 Train Loss: 0.0884 Acc: 0.9377 Pre: 0.9292 Recall: 0.9476 F1: 0.9383 Train AUC: 0.9934 Val AUC: 0.9819 Val PRC: 0.9825 Time: 0.70\n",
      "Epoch: 484 Train Loss: 0.0973 Acc: 0.9377 Pre: 0.9583 Recall: 0.9152 F1: 0.9363 Train AUC: 0.9918 Val AUC: 0.9821 Val PRC: 0.9819 Time: 0.71\n",
      "Epoch: 485 Train Loss: 0.0904 Acc: 0.9366 Pre: 0.9455 Recall: 0.9267 F1: 0.9360 Train AUC: 0.9930 Val AUC: 0.9818 Val PRC: 0.9826 Time: 0.71\n",
      "Epoch: 486 Train Loss: 0.1015 Acc: 0.9440 Pre: 0.9473 Recall: 0.9403 F1: 0.9438 Train AUC: 0.9931 Val AUC: 0.9833 Val PRC: 0.9845 Time: 0.70\n",
      "Epoch: 487 Train Loss: 0.0891 Acc: 0.9351 Pre: 0.9388 Recall: 0.9309 F1: 0.9348 Train AUC: 0.9932 Val AUC: 0.9830 Val PRC: 0.9847 Time: 0.71\n",
      "Epoch: 488 Train Loss: 0.0978 Acc: 0.9382 Pre: 0.9401 Recall: 0.9361 F1: 0.9381 Train AUC: 0.9917 Val AUC: 0.9840 Val PRC: 0.9856 Time: 0.72\n",
      "Epoch: 489 Train Loss: 0.0851 Acc: 0.9387 Pre: 0.9457 Recall: 0.9309 F1: 0.9383 Train AUC: 0.9938 Val AUC: 0.9836 Val PRC: 0.9850 Time: 0.70\n",
      "Epoch: 490 Train Loss: 0.0904 Acc: 0.9356 Pre: 0.9342 Recall: 0.9372 F1: 0.9357 Train AUC: 0.9929 Val AUC: 0.9831 Val PRC: 0.9845 Time: 0.70\n",
      "Epoch: 491 Train Loss: 0.0800 Acc: 0.9403 Pre: 0.9440 Recall: 0.9361 F1: 0.9401 Train AUC: 0.9947 Val AUC: 0.9831 Val PRC: 0.9848 Time: 0.74\n",
      "Epoch: 492 Train Loss: 0.0921 Acc: 0.9377 Pre: 0.9419 Recall: 0.9330 F1: 0.9374 Train AUC: 0.9925 Val AUC: 0.9835 Val PRC: 0.9850 Time: 0.74\n",
      "Epoch: 493 Train Loss: 0.0850 Acc: 0.9393 Pre: 0.9393 Recall: 0.9393 F1: 0.9393 Train AUC: 0.9936 Val AUC: 0.9834 Val PRC: 0.9843 Time: 0.75\n",
      "Epoch: 494 Train Loss: 0.0918 Acc: 0.9398 Pre: 0.9260 Recall: 0.9560 F1: 0.9408 Train AUC: 0.9921 Val AUC: 0.9837 Val PRC: 0.9849 Time: 0.71\n",
      "Epoch: 495 Train Loss: 0.0822 Acc: 0.9366 Pre: 0.9408 Recall: 0.9319 F1: 0.9363 Train AUC: 0.9937 Val AUC: 0.9828 Val PRC: 0.9844 Time: 0.70\n",
      "Epoch: 496 Train Loss: 0.0892 Acc: 0.9330 Pre: 0.9198 Recall: 0.9487 F1: 0.9340 Train AUC: 0.9932 Val AUC: 0.9826 Val PRC: 0.9842 Time: 0.72\n",
      "Epoch: 497 Train Loss: 0.0941 Acc: 0.9387 Pre: 0.9429 Recall: 0.9340 F1: 0.9385 Train AUC: 0.9923 Val AUC: 0.9838 Val PRC: 0.9855 Time: 0.71\n",
      "Epoch: 498 Train Loss: 0.0921 Acc: 0.9340 Pre: 0.9278 Recall: 0.9414 F1: 0.9345 Train AUC: 0.9929 Val AUC: 0.9825 Val PRC: 0.9844 Time: 0.72\n",
      "Epoch: 499 Train Loss: 0.0841 Acc: 0.9398 Pre: 0.9459 Recall: 0.9330 F1: 0.9394 Train AUC: 0.9937 Val AUC: 0.9834 Val PRC: 0.9842 Time: 0.71\n",
      "Epoch: 500 Train Loss: 0.0831 Acc: 0.9366 Pre: 0.9503 Recall: 0.9215 F1: 0.9357 Train AUC: 0.9941 Val AUC: 0.9839 Val PRC: 0.9855 Time: 0.72\n",
      "Fold: 5 Best Epoch: 426 Val acc: 0.9398 Val Pre: 0.9506 Val Recall: 0.9277 Val F1: 0.9391 Val AUC: 0.9840 Val PRC: 0.9850\n",
      "## Training Finished !\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Acc [0.9417, 0.9380, 0.9459, 0.9407, 0.9398]\n",
      "Pre [0.9536, 0.9353, 0.9483, 0.9439, 0.9506]\n",
      "Recall [0.9286, 0.9412, 0.9433, 0.937, 0.9277]\n",
      "F1 [0.9409, 0.9382, 0.9458, 0.9404, 0.9391]\n",
      "Auc [0.9838, 0.9840, 0.9857, 0.9847, 0.9840]\n",
      "Prc [0.9853, 0.9852, 0.9864, 0.9866, 0.9850]\n",
      " AUC mean: 0.9844, variance: 0.0007 \n",
      " Accuracy mean: 0.9412, variance: 0.0026 \n",
      " Precision mean: 0.9463, variance: 0.0064 \n",
      " Recall mean: 0.9356, variance: 0.0064 \n",
      " F1-score mean: 0.9409, variance: 0.0026 \n",
      " PRC mean: 0.9857, variance: 0.0007 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",

      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 
      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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "# device = torch.device('cpu')\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "# training parameters\n",
    "parser.add_argument('--seed', type=int, default=0, help='Random seed.')\n",
    "parser.add_argument('--epochs', type=int, default=500,\n",
    "                    help='Number of epochs to train.')\n",
    "parser.add_argument('--weight_decay', type=float, default=5e-4,\n",
    "                    help='Weight decay (L2 loss on parameters).')\n",
    "parser.add_argument('--dropout', type=float, default=0.1,\n",
    "                    help='Dropout rate (1 - keep probability).')\n",
    "parser.add_argument('--tot_updates',  type=int, default=1000,\n",
    "                        help='used for optimizer learning rate scheduling')\n",
    "parser.add_argument('--warmup_updates', type=int, default=400,\n",
    "                        help='warmup steps')\n",
    "parser.add_argument('--peak_lr', type=float, default=0.001, \n",
    "                        help='Initial learning rate')\n",
    "parser.add_argument('--end_lr', type=float, default=0.0001,  \n",
    "                        help='Final learning rate')\n",
    "# model parameters\n",
    "parser.add_argument('--pe_dim', type=int, default=15,\n",
    "                        help='position embedding size')\n",
    "parser.add_argument('--hops', type=int, default=7,\n",
    "                        help='Hop of neighbors to be calculated')\n",
    "parser.add_argument('--graphformer_layers', type=int, default=1,\n",
    "                    help='number of Graphormer layers')\n",
    "parser.add_argument('--n_heads', type=int, default=8,\n",
    "                    help='number of attention heads in Rgcgt.')\n",
    "parser.add_argument('--node_input', type=int, default=64,\n",
    "                    help='input dimensions of node features/PCA.')\n",
    "parser.add_argument('--node_hidden', type=int, default=128,  \n",
    "                    help='hidden dimensions of node features.')\n",
    "parser.add_argument('--node_output', type=int, default=64,  \n",
    "                    help='output dimensions of node features.')\n",
    "parser.add_argument('--ffn_dim', type=int, default=256,\n",
    "                        help='FFN layer size')\n",
    "parser.add_argument('--GCNII_layers', type=int, default=20,\n",
    "                    help='number of GCNII layers .')\n",
    "# Use parse_known_args to ignore unknown args\n",
    "args, unknown = parser.parse_known_args()\n",
    "# args = parser.parse_args()\n",
    "print('args', args)\n",
    "\n",
    "Adj, Dis_adj, Meta_adj, feature, random_index, k_folds = load_data(args.seed, args.node_input)\n",
    "\n",
    "auc_result = []\n",
    "acc_result = []\n",
    "pre_result = []\n",
    "recall_result = []\n",
    "f1_result = []\n",
    "prc_result = []\n",
    "fprs = []\n",
    "tprs = []\n",
    "precisions = []\n",
    "recalls = []\n",
    "print(\"seed=%d, evaluating metabolite-disease....\" % args.seed)\n",
    "for k in range(k_folds):\n",
    "    print(\"------this is %dth cross validation------\" % (k + 1))\n",
    "    Or_train = np.matrix(Adj, copy=True)\n",
    "    val_pos_edge_index = np.array(random_index[k]).T\n",
    "    val_pos_edge_index = torch.tensor(val_pos_edge_index, dtype=torch.long).to(device)\n",
    "    # Negative sampling of validation set\n",
    "    val_neg_edge_index = np.mat(np.where(Or_train < 1)).T.tolist()\n",
    "    random.seed(args.seed)\n",
    "    random.shuffle(val_neg_edge_index)\n",
    "    val_neg_edge_index = val_neg_edge_index[:val_pos_edge_index.shape[1]]\n",
    "    val_neg_edge_index = np.array(val_neg_edge_index).T\n",
    "    val_neg_edge_index = torch.tensor(val_neg_edge_index, dtype=torch.long).to(device)\n",
    "\n",
    "    Or_train[tuple(np.array(random_index[k]).T)] = 0\n",
    "    train_pos_edge_index = np.mat(np.where(Or_train > 0))\n",
    "    train_pos_edge_index = torch.tensor(train_pos_edge_index, dtype=torch.long).to(device)\n",
    "    Or_train_matrix = np.matrix(Adj, copy=True)\n",
    "    Or_train_matrix[tuple(np.array(random_index[k]).T)] = 0\n",
    "    or_adj = constructNet(torch.tensor(Or_train_matrix)).to(device)\n",
    "\n",
    "    # Positional encoding\n",
    "    lpe = laplacian_positional_encoding(or_adj, args.pe_dim).to(device)  # args.pe_dim: Positional encoding dimension\n",
    "    features = torch.cat((feature, lpe), dim=1)  # Equation (16)\n",
    "\n",
    "    # Construct disease similarity network\n",
    "    Dis_network = torch.nonzero(Dis_adj, as_tuple=True)\n",
    "    Dis_network = torch.stack(Dis_network)\n",
    "    dis_data = Data(x=feature[:Adj.shape[1], ], edge_index=Dis_network)\n",
    "\n",
    "    # Construct metabolite similarity network\n",
    "    Meta_network = torch.nonzero(Meta_adj, as_tuple=True)\n",
    "    Meta_network = torch.stack(Meta_network)\n",
    "    meta_data = Data(x=feature[Adj.shape[1]:, ], edge_index=Meta_network)\n",
    "\n",
    "    # Node embedding for t-hop neighbor aggregation\n",
    "    processed_features = re_features(or_adj, features, args.hops).to(device)  # return (N, hops+1, d)\n",
    "\n",
    "    model = TransformerModel(hops=args.hops,\n",
    "                             output_dim=args.node_output,\n",
    "                             input_dim=features.shape[1],\n",
    "                             pe_dim=args.pe_dim,\n",
    "                             num_dis=Adj.shape[1],\n",
    "                             num_meta=Adj.shape[0],\n",
    "                             graphformer_layers=args.graphformer_layers,\n",
    "                             num_heads=args.n_heads,\n",
    "                             hidden_dim=args.node_hidden,\n",
    "                             ffn_dim=args.ffn_dim,\n",
    "                             dropout_rate=args.dropout,\n",
    "                             GCNII_layers=args.GCNII_layers\n",
    "                             ).to(device)\n",
    "    # print(model)\n",
    "    print('total params:', sum(p.numel() for p in model.parameters()))\n",
    "    model.to(device)\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=args.peak_lr, weight_decay=args.weight_decay)\n",
    "    lr_scheduler = PolynomialDecayLR(\n",
    "        optimizer,\n",
    "        warmup_updates=args.warmup_updates,\n",
    "        tot_updates=args.tot_updates,\n",
    "        lr=args.peak_lr,\n",
    "        end_lr=args.end_lr,\n",
    "        power=1.0,\n",
    "    )\n",
    "    criterion = F.binary_cross_entropy\n",
    "    best_epoch = 0\n",
    "    best_auc = 0\n",
    "    best_acc = 0\n",
    "    best_prc = 0\n",
    "    best_tpr = 0\n",
    "    best_fpr = 0\n",
    "    best_recall = 0\n",
    "    best_precision = 0\n",
    "    for epoch in range(args.epochs):\n",
    "        start = time.time()\n",
    "\n",
    "        model.train()\n",
    "        optimizer.zero_grad()\n",
    "        train_neg_edge_index = np.mat(np.where(Or_train_matrix < 1)).T.tolist()\n",
    "        random.shuffle(train_neg_edge_index)\n",
    "        train_neg_edge_index = train_neg_edge_index[:train_pos_edge_index.shape[1]]\n",
    "        train_neg_edge_index = np.array(train_neg_edge_index).T\n",
    "        train_neg_edge_index = torch.tensor(train_neg_edge_index, dtype=torch.long).to(device)\n",
    "\n",
    "        output = model(processed_features, dis_data, meta_data).to(device)\n",
    "\n",
    "        edge_index = torch.cat([train_pos_edge_index, train_neg_edge_index], 1)\n",
    "        trian_scores = output[edge_index[0], edge_index[1]].to(device)\n",
    "        trian_labels = get_link_labels(train_pos_edge_index, train_neg_edge_index).to(device)\n",
    "        loss_train = criterion(trian_scores, trian_labels).to(device)\n",
    "        loss_train.backward(retain_graph=True)\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            score_train_cpu = np.squeeze(trian_scores.detach().cpu().numpy())\n",
    "            label_train_cpu = np.squeeze(trian_labels.detach().cpu().numpy())\n",
    "            train_auc = metrics.roc_auc_score(label_train_cpu, score_train_cpu)\n",
    "\n",
    "            predict_y_proba = output.reshape(Adj.shape[0], Adj.shape[1]).to(device)\n",
    "            score_val, label_val, metric_tmp = cv_model_evaluate(predict_y_proba, val_pos_edge_index, val_neg_edge_index)\n",
    "\n",
    "            fpr, tpr, thresholds = metrics.roc_curve(label_val, score_val)\n",
    "            precision, recall, _ = metrics.precision_recall_curve(label_val, score_val)\n",
    "            val_auc = metrics.auc(fpr, tpr)\n",
    "            val_prc = metrics.auc(recall, precision)\n",
    "\n",
    "            end = time.time()\n",
    "            print('Epoch:', epoch + 1, 'Train Loss: %.4f' % loss_train.item(),\n",
    "                  'Acc: %.4f' % metric_tmp[0], 'Pre: %.4f' % metric_tmp[1], 'Recall: %.4f' % metric_tmp[2],\n",
    "                  'F1: %.4f' % metric_tmp[3],\n",
    "                  'Train AUC: %.4f' % train_auc, 'Val AUC: %.4f' % val_auc, 'Val PRC: %.4f' % val_prc,\n",
    "                  'Time: %.2f' % (end - start))\n",
    "            if metric_tmp[0] > best_acc and val_auc > best_auc and val_prc > best_prc:\n",
    "                metric_tmp_best = metric_tmp\n",
    "                best_auc = val_auc\n",
    "                best_prc = val_prc\n",
    "                best_epoch = epoch + 1\n",
    "                best_tpr = tpr\n",
    "                best_fpr = fpr\n",
    "                best_recall = recall\n",
    "                best_precision = precision\n",
    "    print('Fold:', k + 1, 'Best Epoch:', best_epoch, 'Val acc: %.4f' % metric_tmp_best[0],\n",
    "              'Val Pre: %.4f' % metric_tmp_best[1],\n",
    "              'Val Recall: %.4f' % metric_tmp_best[2], 'Val F1: %.4f' % metric_tmp_best[3], 'Val AUC: %.4f' % best_auc,\n",
    "              'Val PRC: %.4f' % best_prc,\n",
    "              )\n",
    "\n",
    "    acc_result.append(metric_tmp_best[0])\n",
    "    pre_result.append(metric_tmp_best[1])\n",
    "    recall_result.append(metric_tmp_best[2])\n",
    "    f1_result.append(metric_tmp_best[3])\n",
    "    auc_result.append(round(best_auc, 4))\n",
    "    prc_result.append(round(best_prc, 4))\n",
    "\n",
    "    fprs.append(best_fpr)\n",
    "    tprs.append(best_tpr)\n",
    "    recalls.append(best_recall)\n",
    "    precisions.append(best_precision)\n",
    "\n",
    "print('## Training Finished !')\n",
    "print('-----------------------------------------------------------------------------------------------')\n",
    "print('Acc', acc_result)\n",
    "print('Pre', pre_result)\n",
    "print('Recall', recall_result)\n",
    "print('F1', f1_result)\n",
    "print('Auc', auc_result)\n",
    "print('Prc', prc_result)\n",
    "print('AUC mean: %.4f, variance: %.4f \\n' % (np.mean(auc_result), np.std(auc_result)),\n",
    "        'Accuracy mean: %.4f, variance: %.4f \\n' % (np.mean(acc_result), np.std(acc_result)),\n",
    "        'Precision mean: %.4f, variance: %.4f \\n' % (np.mean(pre_result), np.std(pre_result)),\n",
    "        'Recall mean: %.4f, variance: %.4f \\n' % (np.mean(recall_result), np.std(recall_result)),\n",
    "        'F1-score mean: %.4f, variance: %.4f \\n' % (np.mean(f1_result), np.std(f1_result)),\n",
    "        'PRC mean: %.4f, variance: %.4f \\n' % (np.mean(prc_result), np.std(prc_result)))\n",
    "\n",
    "plot_auc_curves(fprs, tprs, auc_result, directory='../result', name='test_auc')\n",
    "plot_prc_curves(precisions, recalls, prc_result, directory='../result', name='test_prc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bd388e-7836-42dd-a176-be43ef115d79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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